import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import mpl_toolkits as mplot3d
import scipy as sp
pd.set_option("display.max_columns", None)
from tqdm import tqdm
tqdm.pandas()

def lighten_color(color, amount=0.5):
    """
    Lightens the given color by multiplying (1-luminosity) by the given amount.
    Input can be matplotlib color string, hex string, or RGB tuple.

    Examples:
    >> lighten_color('g', 0.3)
    >> lighten_color('#F034A3', 0.6)
    >> lighten_color((.3,.55,.1), 0.5)
    """
    import matplotlib.colors as mc
    import colorsys
    try:
        c = mc.cnames[color]
    except:
        c = color
    c = colorsys.rgb_to_hls(*mc.to_rgb(c))
    return colorsys.hls_to_rgb(c[0], 1 - amount * (1 - c[1]), c[2])

d0 = pd.read_excel("risk literacy_985 responses.xlsx", sheet_name = "643 above median").rename({"#":"ResponseId"}, axis = 1)
d0 = d0.loc[ (d0["Please state your current occupation."] != "Student") ]
d0

d0["Please state your current occupation."].value_counts()

Please state your current occupation.
Employee/Consultant in Other than the Finance Sector                                                                                                                                        378
Investment Professional, i.e. stock broker/trader; financial planner/advisor; portfolio manager; investment banker; stock analyst; venture capital/private equity; insurance agent, etc.    154
Entrepreneur or Own Business                                                                                                                                                                 96
Home Maker or not employed                                                                                                                                                                    5
Retired person                                                                                                                                                                                3
Name: count, dtype: int64

# Age groups/rec = [2,3,4]
# Age group 2 = 18 to 35 y/o
# Age group 3 = 36 to 55 y/o
# Age group 4 = 56 to 75 y/o (75 y/o, i.e, within the scope of the data we have, it can mean 60 and above also)

d0["age_rec"] = np.where( d0["Please mark your age (in years)"] == "18 - 25" , 2,
                          np.where(d0["Please mark your age (in years)"] == "25 - 35", 2,
                                   np.where( d0["Please mark your age (in years)"] == "36 - 45", 3,
                                            np.where( d0["Please mark your age (in years)"] == "46 - 55", 3,
                                                     np.where( d0["Please mark your age (in years)"] == "56 - 65", 4,
                                                              np.where( d0["Please mark your age (in years)"] == "Above 65", 4, 0
                                                                      )
                                                             )
                                                    )
                                           )
                                  )
                        )

d0["age"] = np.where( d0["Please mark your age (in years)"] == "18 - 25" , (18 + 25) / 2,
                          np.where(d0["Please mark your age (in years)"] == "25 - 35", (26 + 35) / 2,
                                   np.where( d0["Please mark your age (in years)"] == "36 - 45", (36 + 45) / 2,
                                            np.where( d0["Please mark your age (in years)"] == "46 - 55", (46 + 55) / 2,
                                                     np.where( d0["Please mark your age (in years)"] == "56 - 65", (56 + 65) / 2,
                                                              np.where( d0["Please mark your age (in years)"] == "Above 65", (66 + 75) / 2, 0
                                                                      )
                                                             )
                                                    )
                                           )
                                  )
                        )


# Secondary upto 10 – ISCED 3
# Senior Secondary upto 12 - ISCED 3 
# Diploma and voca – ISCED 2
# UG – ISCED 2
# PG – ISCED 1
# PhD and higher – ISCED 1

d0["isced"] = np.where(d0["What is currently your highest Education?"] == "Post Graduate", 1,
                        np.where(d0["What is currently your highest Education?"] == "Under Graduate", 2,
                                 np.where(d0["What is currently your highest Education?"] == "Ph.D. or higher", 1,
                                          np.where(d0["What is currently your highest Education?"] == "Diploma or vocation training", 2,
                                                   np.where(d0["What is currently your highest Education?"] == "School degree (X or XII)", 3, 0
                                                           )
                                                  )
                                         )
                                )
                       )

d0["income"] = np.where(d0["Please indicate your approximate annual personal income from all sources for last year"] == "&lt; 5,00,000", 1,
                         np.where(d0["Please indicate your approximate annual personal income from all sources for last year"] == "5,00,000 - 15,00,000", 2,
                                  np.where(d0["Please indicate your approximate annual personal income from all sources for last year"] == "15,00,001 - 25,00,000", 3,
                                           np.where(d0["Please indicate your approximate annual personal income from all sources for last year"] == "25,00,001 - 35,00,000", 4,
                                                    np.where(d0["Please indicate your approximate annual personal income from all sources for last year"] == "35,00,001 - 45,00,000", 4,
                                                             np.where( d0["Please indicate your approximate annual personal income from all sources for last year"] == "&gt; 45,00,000", 5,0
                                                                     )
                                                            )
                                                   )
                                           )
                                  )
                         )

d0["wealth"] = d0["Please provide a rough guess (in Indian Rupees) of the worth of your household's assets. Please do not forget to correct it for your debts, such as a mortgage or any loans you might have."]

matchCol = ["q8_2_1", "q8_2_2", "q8_2_3", "q8_2_4", "q8_2_5", "q8_3", "q8_4", "q8_5berlin_1", 
            "q8_5london_1", "q8_5paris_1", "q8_6", "q8_7", "q9_1_1", "q9_2_1", "q9_3",
            "q10_1_1", "q10_2_1", "q10_3_1", "q10_4"]
d0[ matchCol ] = 0

d0.columns

Index(['ResponseId', 'Please indicate your Gender.',
       'Please mark your age (in years)',
       'What is currently your highest Education?',
       'Please state your current occupation.',
       'How do you describe your willingness to take financial risk in general?',
       'Given the number of years that you have held various investments and the amount of investing you might have done, what degree of investment experience in the stock market do you have?',
       'If an expert tries to worry or scare me, i.e. a financial advisor about my financial situation, I choose another expert.',
       'I only buy a financial product I understand.', 'I trust doctors.',
       'When I want to buy a bigger item like a refrigerator or an expensive item of clothing, I wait a month to see whether I still want it and only buy it then.',
       'I always keep in mind that everything I do on the web could be used to my disadvantage.',
       'In my household, we/I spend:', 'In my household, we/I',
       '_Distribution 1_\n\nHow risky do you perceive the investment to be?',
       '_Distribution 2_\n\nHow risky do you perceive the investment to be?',
       '_Distribution 3_\n\nHow risky do you perceive the investment to be?',
       '_Distribution 4_\n\nHow risky do you perceive the investment to be?',
       '_Distribution 5_\n\nHow risky do you perceive the investment to be?',
       'Distribution 6\n\nHow risky do you perceive the investment to be?',
       'Distribution 7\n\nHow risky do you perceive the investment to be?',
       'Distribution 8\n\nHow risky do you perceive the investment to be?',
       '*Mumbai * A = 9 out of 10000   *OR   * B = 1 out of 1000',
       '*Bengaluru * A = 0.7%   *OR  * B = 0.099%',
       '*Kolkata * A = 0.61%   *OR   * B = 6 out of 10000', 'HIV test',
       'Fingerprint', 'DNA test', 'Cancer screening test',
       'Professional horoscope',
       'A study estimates that eating 100g chocolate everyday increases the risk of obesity by 20%.  Which of the following statements is true?',
       'There is an official prediction that the national stock market will grow 2% annually over the next 5 years.  This means that…',
       'Imagine you are told that the price of the stock Soya Ruchi increases from INR 60 to INR 120 after the company merger. What does this mean?',
       'It is predicted that Indigo Bank has 30% chance of default next year. Which of the following alternatives is the most appropriate interpretation of the statement?',
       'The probability that the economy will go into a recession this year is 30%. If the economy goes into recession, the probability that the stock market will decrease is 80%. If the economy does not go into a recession, the probability that the stock market will decrease is 23%. What is the probability that the economy goes into recession given that the stock market decreased?',
       'A new policy intervention increases the number of people who are employed by 20%. This statistic implies that the intervention increases the number of people who are employed from:',
       'Imagine you are told that a new medication increases the number of people who recover from a disease from 2 out of 1,000 to 4 out of 1,000.  This implies:',
       'Imagine that we flip a fair coin 1,000 times. What is your best guess about how many times the coin will come up heads in 1,000 flips? \n\n\_\_\_\_\_\_ times out of 1,000.',
       'In the Bingo Lottery, the chance of winning a $10 prize is 1%. What is your best guess about how many people will win a $10 prize if 1,000 people each buy a single ticket for Bingo Lottery?\n\n\_\_\_\_\_\_ person(s) out of 1,000.',
       'In a sweepstakes, the chance of winning a car is 1 in 1,000. What percentage of tickets for the sweepstakes wins a car?\n\n\_\_\_\_\_ % of tickets',
       'About 10 out of 1,000 children develop Down syndrome. In a Down syndrome test, 9 out of these 10 children with Down syndrome tested positive. Out of the 990 children without Down syndrome 50 nevertheless tested positive. Among those women with a positive test result concerning their child how many actually have a child with Down syndrome? [Select one response only]',
       'Approximately what percentage (%) of people who die from cancer die from colon cancer, breast cancer, and prostate cancer taken together?',
       'The following figure shows the number of men and women among a group of smartphone users. The total number of circles is 100. \n\nHow many more men than women are there among the 100 people using a smartphone?',
       'In a magazine you see two advertisements, one on page 5 and another on page 12. Each is for a different drug for treating heart disease, and each includes a graph showing the effectiveness of the drug compared to a placebo (sugar pill).\n\nCompared to the placebo, which treatment leads to a larger decrease in the percentage of patients who die?',
       'Please indicate your approximate annual personal income from all sources for last year',
       'Please provide a rough guess (in Indian Rupees) of the worth of your household's assets. Please do not forget to correct it for your debts, such as a mortgage or any loans you might have.',
       'uid', 'Response Type', 'Start Date (UTC)', 'Stage Date (UTC)',
       'Submit Date (UTC)', 'Network ID', 'LTA', 'age_rec', 'age', 'isced',
       'income', 'wealth', 'q8_2_1', 'q8_2_2', 'q8_2_3', 'q8_2_4', 'q8_2_5',
       'q8_3', 'q8_4', 'q8_5berlin_1', 'q8_5london_1', 'q8_5paris_1', 'q8_6',
       'q8_7', 'q9_1_1', 'q9_2_1', 'q9_3', 'q10_1_1', 'q10_2_1', 'q10_3_1',
       'q10_4'],
      dtype='object')

d0["HIV test"].value_counts()

HIV test
Yes    430
No     206
Name: count, dtype: int64

d0["q8_2_1"] = np.where(d0["HIV test"] == "Yes", 1, 2)
d0["q8_2_2"] = np.where(d0["Fingerprint"] == "Yes", 1, 2)
d0["q8_2_3"] = np.where(d0["DNA test"] == "Yes", 1, 2)
d0["q8_2_4"] = np.where(d0["Cancer screening test"] == "Yes", 1, 2)
d0["q8_2_5"] = np.where(d0["Professional horoscope"] == "Yes", 1, 2)

d0["q8_3"] = np.where(d0["A study estimates that eating 100g chocolate everyday increases the risk of obesity by 20%.  Which of the following statements is true?"] == "The lower the quality of the study, the more likely that future studies will change the risk estimate.", 1,
                          np.where(d0["A study estimates that eating 100g chocolate everyday increases the risk of obesity by 20%.  Which of the following statements is true?"] == "The higher the quality of the study, the more likely that future studies will change the risk estimate.", 2,
                                  np.where(d0["A study estimates that eating 100g chocolate everyday increases the risk of obesity by 20%.  Which of the following statements is true?"] == "Irrespective of the quality of the study, future studies will not change the risk estimate.", 3,
                                          np.where(d0["A study estimates that eating 100g chocolate everyday increases the risk of obesity by 20%.  Which of the following statements is true?"] == "Irrespective of the quality of the study, future studies will change the risk estimate substantially anyway.", 4,0
                                                  )
                                          )
                                  )
                         )


d0["q8_4"] = np.where(d0["There is an official prediction that the national stock market will grow 2% annually over the next 5 years.  This means that…"] == "the growth rate will be 0.4% on average each year", 1,
                          np.where(d0["There is an official prediction that the national stock market will grow 2% annually over the next 5 years.  This means that…"] == "the growth rate over five years will be exactly 2%", 2,
                                  np.where(d0["There is an official prediction that the national stock market will grow 2% annually over the next 5 years.  This means that…"] == "the growth rate over five years will be between 1% and 3%", 3,
                                          np.where(d0["There is an official prediction that the national stock market will grow 2% annually over the next 5 years.  This means that…"] == "it is not possible to predict the growth rate with certainty.", 4,0
                                                  )
                                          )
                                  )
                         )

d0["q8_5berlin_1"] = np.where(d0["*Mumbai * A = 9 out of 10000   *OR   * B = 1 out of 1000"] == "A", 1, 2)
d0["q8_5london_1"] = np.where(d0["*Bengaluru * A = 0.7%   *OR  * B = 0.099%"] == "A", 1, 2)
d0["q8_5paris_1"] = np.where(d0["*Kolkata * A = 0.61%   *OR   * B = 6 out of 10000"] == "A", 1, 2)

d0["q8_6"] = np.where(d0["Imagine you are told that a new medication increases the number of people who recover from a disease from 2 out of 1,000 to 4 out of 1,000.  This implies:"] == "The medication increases recovery by 100%", 1,
                          np.where(d0["Imagine you are told that a new medication increases the number of people who recover from a disease from 2 out of 1,000 to 4 out of 1,000.  This implies:"] == "The medication increases recovery by 50%", 2,
                                   np.where(d0["Imagine you are told that a new medication increases the number of people who recover from a disease from 2 out of 1,000 to 4 out of 1,000.  This implies:"] == "The medication increases recovery by 2%", 3,
                                            np.where(d0["Imagine you are told that a new medication increases the number of people who recover from a disease from 2 out of 1,000 to 4 out of 1,000.  This implies:"] == "None of the above is implied.", 4, 0
                                                    )
                                           )
                                  )
                         )

d0["q8_7"] = np.where(d0["A new policy intervention increases the number of people who are employed by 20%. This statistic implies that the intervention increases the number of people who are employed from:"] == "5 in 100 people prior to the intervention to 6 out of 100 people after the intervention", 1,
                          np.where(d0["A new policy intervention increases the number of people who are employed by 20%. This statistic implies that the intervention increases the number of people who are employed from:"] == "100 in 10,000 people prior to the intervention to 120 out of 10,000 people after the intervention", 2,
                                   np.where(d0["A new policy intervention increases the number of people who are employed by 20%. This statistic implies that the intervention increases the number of people who are employed from:"] == "70 in 100 people prior to the intervention to 90 out of 100 people after the intervention", 3,
                                            np.where(d0["A new policy intervention increases the number of people who are employed by 20%. This statistic implies that the intervention increases the number of people who are employed from:"] == "it is not possible to determine which of the answers is correct given the information provided", 4, 0
                                                    )
                                           )
                                  )
                         )


d0["q9_1_1"] = d0["Approximately what percentage (%) of people who die from cancer die from colon cancer, breast cancer, and prostate cancer taken together?"].copy()
d0["q9_2_1"] = d0["The following figure shows the number of men and women among a group of smartphone users. The total number of circles is 100. \n\nHow many more men than women are there among the 100 people using a smartphone?"].copy()

d0["q9_3"] = np.where(d0["In a magazine you see two advertisements, one on page 5 and another on page 12. Each is for a different drug for treating heart disease, and each includes a graph showing the effectiveness of the drug compared to a placebo (sugar pill).\n\nCompared to the placebo, which treatment leads to a larger decrease in the percentage of patients who die?"] == "Crosicol", 1,
                          np.where(d0["In a magazine you see two advertisements, one on page 5 and another on page 12. Each is for a different drug for treating heart disease, and each includes a graph showing the effectiveness of the drug compared to a placebo (sugar pill).\n\nCompared to the placebo, which treatment leads to a larger decrease in the percentage of patients who die?"] == "Hertinol", 2,
                                   np.where(d0["In a magazine you see two advertisements, one on page 5 and another on page 12. Each is for a different drug for treating heart disease, and each includes a graph showing the effectiveness of the drug compared to a placebo (sugar pill).\n\nCompared to the placebo, which treatment leads to a larger decrease in the percentage of patients who die?"] == "They are equal", 3,
                                            np.where(d0["In a magazine you see two advertisements, one on page 5 and another on page 12. Each is for a different drug for treating heart disease, and each includes a graph showing the effectiveness of the drug compared to a placebo (sugar pill).\n\nCompared to the placebo, which treatment leads to a larger decrease in the percentage of patients who die?"] == "Can’t say", 4, 0
                                                    )
                                           )
                                  )
                         )

d0["q10_1_1"] = d0["Imagine that we flip a fair coin 1,000 times. What is your best guess about how many times the coin will come up heads in 1,000 flips? \n\n\_\_\_\_\_\_ times out of 1,000."].copy()
d0["q10_2_1"] = d0["In the Bingo Lottery, the chance of winning a $10 prize is 1%. What is your best guess about how many people will win a $10 prize if 1,000 people each buy a single ticket for Bingo Lottery?\n\n\_\_\_\_\_\_ person(s) out of 1,000."].copy()
d0["q10_3_1"] = d0["In a sweepstakes, the chance of winning a car is 1 in 1,000. What percentage of tickets for the sweepstakes wins a car?\n\n\_\_\_\_\_ % of tickets"].copy()

d0["q10_4"] = np.where(d0["About 10 out of 1,000 children develop Down syndrome. In a Down syndrome test, 9 out of these 10 children with Down syndrome tested positive. Out of the 990 children without Down syndrome 50 nevertheless tested positive. Among those women with a positive test result concerning their child how many actually have a child with Down syndrome? [Select one response only]"] == "9 out of 59", 1,
                          np.where(d0["About 10 out of 1,000 children develop Down syndrome. In a Down syndrome test, 9 out of these 10 children with Down syndrome tested positive. Out of the 990 children without Down syndrome 50 nevertheless tested positive. Among those women with a positive test result concerning their child how many actually have a child with Down syndrome? [Select one response only]"] == "9 out of 10", 2,
                                   np.where(d0["About 10 out of 1,000 children develop Down syndrome. In a Down syndrome test, 9 out of these 10 children with Down syndrome tested positive. Out of the 990 children without Down syndrome 50 nevertheless tested positive. Among those women with a positive test result concerning their child how many actually have a child with Down syndrome? [Select one response only]"] == "59 out of 1000", 3,
                                            np.where(d0["About 10 out of 1,000 children develop Down syndrome. In a Down syndrome test, 9 out of these 10 children with Down syndrome tested positive. Out of the 990 children without Down syndrome 50 nevertheless tested positive. Among those women with a positive test result concerning their child how many actually have a child with Down syndrome? [Select one response only]"] == "59 out of 100", 4, 0
                                                    )
                                           )
                                  )
                         )

scoreColumns = ["certainty1", "certainty2", "certainty3", "certainty4", "certainty5", "uncertainty1", "uncertainty2", "numeracy1", "numeracy2", "numeracy3", "numeracy4", "numeracy5", "graph1", "graph2", "graph3", "riskcomprehension1", "riskcomprehension2", "riskcomprehension3", "riskcomprehension4", "riskcomprehension5", "bayesian1"]
d0[scoreColumns] = 0

# Assigning scores

def scoring1(surveyFacet):
    surveyFacet.loc[ surveyFacet["q8_2_1"] == 2, "certainty1"] = 1
    surveyFacet.loc[ surveyFacet["q8_2_2"] == 2, "certainty2"] = 1
    surveyFacet.loc[ surveyFacet["q8_2_3"] == 2, "certainty3"] = 1
    surveyFacet.loc[ surveyFacet["q8_2_4"] == 2, "certainty4"] = 1
    surveyFacet.loc[ surveyFacet["q8_2_5"] == 2, "certainty5"] = 1

    surveyFacet.loc[ surveyFacet["q8_3"] == 1, "uncertainty1"] = 1
    surveyFacet.loc[ surveyFacet["q8_4"] == 4, "uncertainty2"] = 1

    surveyFacet.loc[ surveyFacet["q8_5berlin_1"] == 2, "riskcomprehension1"] = 1
    surveyFacet.loc[ surveyFacet["q8_5london_1"] == 1, "riskcomprehension2"] = 1
    surveyFacet.loc[ surveyFacet["q8_5paris_1"] == 1, "riskcomprehension3"] = 1
    
    surveyFacet.loc[ surveyFacet["q8_6"] == 1, "riskcomprehension4"] = 1
    surveyFacet.loc[ surveyFacet["q8_7"] == 4, "riskcomprehension5"] = 1

    surveyFacet.loc[ surveyFacet["q9_1_1"] == 25, "graph1"] = 1
    surveyFacet.loc[ surveyFacet["q9_2_1"] == 20, "graph2"] = 1
    surveyFacet.loc[ surveyFacet["q9_3"] == 3, "graph3"] = 1

    surveyFacet.loc[ surveyFacet["q10_1_1"] == 500, "numeracy1"] = 1
    surveyFacet.loc[ surveyFacet["q10_2_1"] == 10, "numeracy2"] = 1
    surveyFacet.loc[ (surveyFacet["q10_3_1"] == 0.1) | (surveyFacet["q10_3_1"] == ".1") | (surveyFacet["q10_3_1"] == ",1") , "numeracy3"] = 1
    surveyFacet.loc[ surveyFacet["q10_4"] == 1, "bayesian1"] = 1

    # Assigning total scores
    # surveyFacet["Certainty score_5"] = surveyFacet["certainty1"] + surveyFacet["certainty2"] + surveyFacet["certainty3"] + surveyFacet["certainty4"] + surveyFacet["certainty5"]
    #surveyFacet["Uncertainty score_5"] = surveyFacet["uncertainty1"] + surveyFacet["uncertainty2"]

    #surveyFacet["Number Comprehension score_5"] = surveyFacet["numeracy1"] + surveyFacet["numeracy2"] + surveyFacet["numeracy3"] + surveyFacet["numeracy4"] + surveyFacet["numeracy5"]
    #surveyFacet["Graph Comprehension score_5"] = surveyFacet["graph1"] + surveyFacet["graph2"] + surveyFacet["graph3"]

    #surveyFacet["Calculation score_4"] = surveyFacet["riskcalculation1"] + surveyFacet["riskcalculation2"] + surveyFacet["riskcalculation3"] + surveyFacet["riskcalculation4"]

    #surveyFacet["Total Score_19"] = surveyFacet["Certainty score_5"] + surveyFacet["Uncertainty score_5"] + surveyFacet["Number Comprehension score_5"] + surveyFacet["Graph Comprehension score_5"] + surveyFacet["Calculation score_4"]

    return surveyFacet

d0 = d0.groupby(["ResponseId"]).progress_apply(scoring1)
d0

100%|███████████████████████████████████████████████████████████████████████████████| 636/636 [00:04<00:00, 149.16it/s]

def scoring2(survey):
    
    survey["Certainty_5"] = survey["certainty1"] + survey["certainty2"] + survey["certainty3"] + survey["certainty4"] + survey["certainty5"]
    
    survey["Uncertainty_2"] = survey["uncertainty1"] + survey["uncertainty2"]
    
    survey["RiskComprehension_5"] = survey["riskcomprehension1"] + survey["riskcomprehension2"] + survey["riskcomprehension3"] + survey["riskcomprehension4"] + survey["riskcomprehension5"]
    
    survey["GraphLiteracy_3"] = survey["graph1"] + survey["graph2"] + survey["graph3"]
    
    survey["Numeracy_2"] = survey["numeracy1"] + survey["numeracy2"] # + survey["numeracy3"]
    
    survey["Bayesianreasoning_1"] = survey["bayesian1"]
    
    survey["TotalScore_18"] = survey["Certainty_5"] + survey["Uncertainty_2"] + survey["RiskComprehension_5"] + survey["GraphLiteracy_3"] + survey["Numeracy_2"] + survey["Bayesianreasoning_1"]
    
    survey["Certainty_%"] = survey["Certainty_5"] / 5 * 100
    survey["Uncertainty_%"] = survey["Uncertainty_2"] / 2 * 100
    survey["RiskComprehension_%"] = survey["RiskComprehension_5"] / 5 * 100
    survey["GraphLiteracy_%"] = survey["GraphLiteracy_3"] / 3 * 100
    survey["Numeracy_%"] = survey["Numeracy_2"] / 2 * 100
    survey["Bayesianreasoning_%"] = survey["Bayesianreasoning_1"] / 1 * 100
    
    survey["TotalScore_%"] = survey["TotalScore_18"] / 18 * 100
    
    
    colReq = ["ResponseId", "age", "age_rec", "isced", "income", "wealth", "Certainty_5", "Uncertainty_2", "RiskComprehension_5", "GraphLiteracy_3",
              "Numeracy_2", "Bayesianreasoning_1", "Certainty_%", "Uncertainty_%", "RiskComprehension_%", "GraphLiteracy_%", "Numeracy_%", "Bayesianreasoning_%",
              "TotalScore_18", "TotalScore_%",]
    
    survey1 = survey[colReq].copy()
    return survey1

d01 = scoring2(d0)
d01

d1 = pd.read_csv('Indian Risk Survey_Besample_Filtered_12_2024.csv')
d11 = d1.iloc[:, 19:62].copy()
d11.insert(0, "ResponseId",0)
d11["ResponseId"] = d1["ResponseId"].copy()
d11 = d11.loc[ (d11["Q4"] != "Student")]

d11["Q4"].value_counts()

Q4
Salaried/Employee/Consultant in a sector other than Finance    167
Not employed                                                    50
Entrepreneur/Business Owner in a sector other than Finance      30
Stock analyst                                                   12
Insurance agent, etc                                             7
Retired                                                          3
Venture capital/private equity                                   1
Name: count, dtype: int64

d11["age"] = d11["Q2"]

# Age groups/rec = [2,3,4]
# Age group 2 = 18 to 35 y/o
# Age group 3 = 36 to 55 y/o
# Age group 4 = 56 to 75 y/o (75 y/o, i.e, within the scope of the data we have, it can mean 60 and above also)

d11["age_rec"] = np.where( (d11["age"] >= 18) & (d11["age"] <= 35), 2,
                          np.where( (d11["age"] >= 36) & (d11["age"] <= 55), 3,
                                   np.where( (d11["age"] >= 56), 4, 0
                                           )
                                  )
                         )


# Secondary upto 10 – ISCED 3
# Senior Secondary upto 12 - ISCED 3 
# Diploma and voca – ISCED 2
# UG – ISCED 2
# PG – ISCED 1
# PhD and higher – ISCED 1

d11["isced"] = np.where(d11["Q3"] == "Post-Graduate Program", 1,
                        np.where(d11["Q3"] == "Undergraduate Program", 2,
                                 np.where(d11["Q3"] == "Ph.D. and higher", 1,
                                          np.where(d11["Q3"] == "Diploma and Vocational Training", 2,
                                                   np.where(d11["Q3"] == "Secondary School (11th to 12th Std.)", 3,
                                                            np.where( d11["Q3"] == "Primary School (up to 10th Std.)", 3,
                                                                     np.where( d11["Q3"] == "M.Phil.", 1, 0
                                                                             )
                                                                    )
                                                           )
                                                  )
                                         )
                                )
                       )

d11["income"] = np.where(d11["Q15a"] == "< INR 500,000", 1,
                         np.where(d11["Q15a"] == "INR 500,001 – INR 15,00,000", 2,
                                  np.where(d11["Q15a"] == "INR 1500,001 – INR 30,00,000", 3,
                                           np.where(d11["Q15a"] == "INR 30,00,001 – INR 50,00,000", 4,
                                                    np.where(d11["Q15a"] == "INR 50,00,001 – INR 75,00,000", 5,
                                                             np.where( d11["Q15a"] == "> INR 75,00,000", 5,0
                                                                     )
                                                            )
                                                   )
                                           )
                                  )
                         )

d11["wealth"] = d11["Q14b.1"]

matchCol = ["q8_2_1", "q8_2_2", "q8_2_3", "q8_2_4", "q8_2_5", "q8_3", "q8_4", "q8_5berlin_1", 
            "q8_5london_1", "q8_5paris_1", "q8_6", "q8_7", "q9_1_1", "q9_2_1", "q9_3",
            "q10_1_1", "q10_2_1", "q10_3_1", "q10_4"]
d11[ matchCol ] = 0

d11.columns

Index(['ResponseId', 'Q0', 'Q1', 'Q2', 'Q3', 'Q4', 'Q5_1', 'Q6_1', 'Q8_1',
       'Q8_2', 'Q8_3', 'Q8_4', 'Q8_5', 'Q8_6', 'Q8_7', 'Q8_8', 'Q9b_1',
       'Q9b_4', 'Q9b_5', 'Q9b_6', 'Q9b_7', 'Q9b_8', 'Q9b_9', 'Q11a_1',
       'Q11a_2', 'Q11a_3', 'Q11b', 'Q11c', 'Q11d', 'Q11h', 'Attention Check',
       'Q11i', 'Q12a', 'Q12b', 'Q12c', 'Q13a', 'Q13b', 'Q13c', 'Q13d', 'Q14a',
       'Q14b', 'Q14c', 'Q15a', 'Q14b.1', 'age', 'age_rec', 'isced', 'income',
       'wealth', 'q8_2_1', 'q8_2_2', 'q8_2_3', 'q8_2_4', 'q8_2_5', 'q8_3',
       'q8_4', 'q8_5berlin_1', 'q8_5london_1', 'q8_5paris_1', 'q8_6', 'q8_7',
       'q9_1_1', 'q9_2_1', 'q9_3', 'q10_1_1', 'q10_2_1', 'q10_3_1', 'q10_4'],
      dtype='object')

d11["q8_2_1"] = np.where(d11["Q11b"].str.contains("HIV test"), 1, 2)
d11["q8_2_2"] = np.where(d11["Q11b"].str.contains("Fingerprint"), 1, 2)
d11["q8_2_3"] = np.where(d11["Q11b"].str.contains("DNA test"), 1, 2)
d11["q8_2_4"] = np.where(d11["Q11b"].str.contains("Cancer screening test"), 1, 2)
d11["q8_2_5"] = np.where(d11["Q11b"].str.contains("Professional horoscope"), 1, 2)

d11["q8_3"] = np.where(d11["Q11c"] == "The lower the quality of the study, the more likely that future studies will change the risk estimate.", 1,
                          np.where(d11["Q11c"] == "The higher the quality of the study, the more likely that future studies will change the risk estimate.", 2,
                                  np.where(d11["Q11c"] == "Irrespective of the quality of the study, future studies will not change the risk estimate.", 3,
                                          np.where(d11["Q11c"] == "Irrespective of the quality of the study, future studies will change the risk estimate substantially anyway.", 4,0
                                                  )
                                          )
                                  )
                         )


d11["q8_4"] = np.where(d11["Q11d"] == "The growth rate will be 0.4% on average each year", 1,
                          np.where(d11["Q11d"] == "The growth rate over five years will be exactly 2%", 2,
                                  np.where(d11["Q11d"] == "The growth rate over five years will be between 1% and 3%", 3,
                                          np.where(d11["Q11d"] == "It is not possible to predict the growth rate with certainty", 4,0
                                                  )
                                          )
                                  )
                         )

d11["q8_5berlin_1"] = np.where(d11["Q11a_1"] == 1, 1, 2)
d11["q8_5london_1"] = np.where(d11["Q11a_1"] == 1, 1, 2)
d11["q8_5paris_1"] = np.where(d11["Q11a_1"] == 1, 1, 2)

d11["q8_6"] = np.where(d11["Q11i"] == "The medication increases recovery by 100%", 1,
                          np.where(d11["Q11i"] == "The medication increases recovery by 50%", 2,
                                   np.where(d11["Q11i"] == "The medication increases recovery by 2%", 3,
                                            np.where(d11["Q11i"] == "None of the above is implied", 4, 0
                                                    )
                                           )
                                  )
                         )

d11["q8_7"] = np.where(d11["Q11h"] == "5 in 100 people prior to the intervention to 6 out of 100 people after the intervention", 1,
                          np.where(d11["Q11h"] == "100 in 10,000 people prior to the intervention to 120 out of 10,000 people after the intervention", 2,
                                   np.where(d11["Q11h"] == "70 in 100 people prior to the intervention to 90 out of 100 people after the intervention", 3,
                                            np.where(d11["Q11h"] == "It is not possible to determine which of the answers is correct given the information provided", 4, 0
                                                    )
                                           )
                                  )
                         )


d11["q9_1_1"] = d11["Q14a"].copy()
d11["q9_2_1"] = d11["Q14b"].copy()

d11["q9_3"] = np.where(d11["Q14c"] == "Crosicol", 1,
                          np.where(d11["Q14c"] == "Hertinol", 2,
                                   np.where(d11["Q14c"] == "They are equal", 3,
                                            np.where(d11["Q14c"] == "Can’t say", 4, 0
                                                    )
                                           )
                                  )
                         )

d11["q10_1_1"] = d11["Q13a"].copy()
d11["q10_2_1"] = d11["Q13b"].copy()
d11["q10_3_1"] = d11["Q13c"].copy()

d11["q10_4"] = np.where(d11["Q13d"] == "9 out of 59", 1,
                          np.where(d11["Q13d"] == "9 out of 10", 2,
                                   np.where(d11["Q13d"] == "59 out of 1000", 3,
                                            np.where(d11["Q13d"] == "59 out of 100", 4, 0
                                                    )
                                           )
                                  )
                         )

d11[ matchCol ]

scoreColumns = ["certainty1", "certainty2", "certainty3", "certainty4", "certainty5", "uncertainty1", "uncertainty2", "numeracy1", "numeracy2", "numeracy3", "numeracy4", "numeracy5", "graph1", "graph2", "graph3", "riskcomprehension1", "riskcomprehension2", "riskcomprehension3", "riskcomprehension4", "riskcomprehension5", "bayesian1"]
d11[scoreColumns] = 0

d11Facet = d11.groupby(["ResponseId"]).progress_apply(scoring1)

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d11Facet

d11 = d11Facet.copy().reset_index(drop = True)

d12 = scoring2(d11)
d12

df2 = pd.concat([d01, d12], axis = 0).reset_index(drop = True)

df2

# The following graph represents frequency of each data point on "TotalScore_18" or the total score out of 13 questions across the sample.

df2['TotalScore_18'].plot(kind = 'hist', xticks = np.arange(0, 20, step=1), xlabel = 'TotalScore_18', title = 'Frequency of Scores' )

<Axes: title={'center': 'Frequency of Scores'}, xlabel='TotalScore_18', ylabel='Frequency'>

# Absolute mean scores for each facet

(df2[['Certainty_5', 'Uncertainty_2', 'RiskComprehension_5','GraphLiteracy_3','Numeracy_2','Bayesianreasoning_1','TotalScore_18']].mean(axis = 0)).plot(kind = 'bar', title = 'Absolute mean of scores for above data set')

<Axes: title={'center': 'Absolute mean of scores for above data set'}>

# Normalised mean scores for each facet

df2[['Certainty_%', "Uncertainty_%", 'RiskComprehension_%','GraphLiteracy_%','Numeracy_%','Bayesianreasoning_%','TotalScore_%']].mean(axis = 0).plot(kind = 'bar', title = '% mean of scores for above data set')

<Axes: title={'center': '% mean of scores for above data set'}>

# Data Frame 5 or df5 is an aggregate data on facet total scores and over all total score, along with  education data vs each response. 
# This data set is now sorted by education.

df5 = df2.sort_values(by = 'isced')
df5 = df5.reset_index(drop = True)

df5

# Mean of each facet as a % for each category of isced

df5.groupby('isced')[['Certainty_%', "Uncertainty_%", 'RiskComprehension_%','Numeracy_%','GraphLiteracy_%','Bayesianreasoning_%']].mean()

# Count of responses for each category isced

df5.groupby('isced')[['ResponseId']].count()

# Absolute Total Facet scores mean line plot from low to high ISCED

df5.groupby('isced')[['TotalScore_18']].mean().plot( kind = 'line', title = 'Absolute Total Facet scores mean line plot', xticks = np.arange(1,4, step = 1)).legend(loc='center left',bbox_to_anchor=(1.0, 0.5))

<matplotlib.legend.Legend at 0x27c38f22c50>

# Absolute Facet scores mean line plot from low to high edu

df5.groupby('isced')[['Certainty_5', "Uncertainty_2", 'RiskComprehension_5','Numeracy_2','GraphLiteracy_3','Bayesianreasoning_1']].mean().plot( kind = 'line', title = 'Absolute Facet scores mean line plot', xticks = np.arange(1,4, step = 1)).legend(loc='center left',bbox_to_anchor=(1.0, 0.5))

<matplotlib.legend.Legend at 0x27c39cfd550>

# Normalised Facet scores mean line plot from low to high edu


df5.groupby('isced')[['Certainty_%', "Uncertainty_%", 'RiskComprehension_%','Numeracy_%','GraphLiteracy_%','Bayesianreasoning_%','TotalScore_%']].mean().plot( kind = 'line', title = 'Normalised Facet scores mean line plot', xticks = np.arange(1,4, step = 1)).legend(loc='center left',bbox_to_anchor=(1.0, 0.5))

<matplotlib.legend.Legend at 0x27c364192d0>

# Normalised mean scores for each facet stacked

df5.groupby('isced')[['Certainty_%', "Uncertainty_%", 'RiskComprehension_%','Numeracy_%','GraphLiteracy_%','Bayesianreasoning_%']].mean().plot( kind = 'bar', title = 'Normalised mean scores for each facet stacked', stacked = True).legend(loc='center left',bbox_to_anchor=(1.0, 0.5))

<matplotlib.legend.Legend at 0x27c3783afd0>

# Normalised mean scores for each facet for each edu response category

df5.groupby('isced')[['Certainty_%', "Uncertainty_%", 'RiskComprehension_%','Numeracy_%','GraphLiteracy_%','Bayesianreasoning_%','TotalScore_%']].mean().T.plot(kind = 'bar', title = 'Normalised mean scores for each facet for each edu response category').legend(loc='center left',bbox_to_anchor=(1.0, 0.5))

<matplotlib.legend.Legend at 0x27c393fa850>

# Trend line for Absolute Total Facet Score vs edu (isced) reponses

sns.regplot (data = df5, x = 'isced', y = 'TotalScore_18')

<Axes: xlabel='isced', ylabel='TotalScore_18'>

# Trend line for Absolute Independent Facet Score vs edu (isced) reponses

fig, ax6 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df5, x = 'isced', y = 'Certainty_5', fit_reg=True, ci=None, ax=ax6, label='Certainty_5')
sns.regplot (data = df5, x = 'isced', y = 'Uncertainty_2', fit_reg=True, ci=None, ax=ax6, label='Uncertainty_2')
sns.regplot (data = df5, x = 'isced', y = 'RiskComprehension_5', fit_reg=True, ci=None, ax=ax6, label='RiskComprehension_5')
sns.regplot (data = df5, x = 'isced', y = 'Numeracy_2', fit_reg=True, ci=None, ax=ax6, label='Numeracy_2')
sns.regplot (data = df5, x = 'isced', y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax6, label='GraphLiteracy_3')
sns.regplot (data = df5, x = 'isced', y = 'Bayesianreasoning_1',fit_reg=True, ci=None, ax=ax6, label='Bayesianreasoning_1' )

ax6.set(ylabel='Scores', xlabel='isced')
ax6.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

# Violine Plot for TotalScore_18 for each entry under edu.

sns.violinplot( data = df5, x = 'isced', y = 'TotalScore_18')

<Axes: xlabel='isced', ylabel='TotalScore_18'>

# Violine Plot for Certainty_5 for each entry under edu.

sns.violinplot( data = df5, x = 'isced', y = 'Certainty_5')

<Axes: xlabel='isced', ylabel='Certainty_5'>

# Violine Plot for Uncertainty_5 for each entry under edu.

sns.violinplot( data = df5, x = 'isced', y = 'Uncertainty_2')

<Axes: xlabel='isced', ylabel='Uncertainty_2'>

# Violine Plot for RiskComprehension_5 for each entry under edu.

sns.violinplot( data = df5, x = 'isced', y = 'RiskComprehension_5')

<Axes: xlabel='isced', ylabel='RiskComprehension_5'>

# Violine Plot for GraphLiteracy_3 for each entry under edu.

sns.violinplot( data = df5, x = 'isced', y = 'GraphLiteracy_3')

<Axes: xlabel='isced', ylabel='GraphLiteracy_3'>

# Violine Plot for Numeracy_2 for each entry under edu.

sns.violinplot( data = df5, x = 'isced', y = 'Numeracy_2')

<Axes: xlabel='isced', ylabel='Numeracy_2'>

# Violine Plot for TotalScore_18 for each entry under edu.

sns.violinplot( data = df5, x = 'isced', y = 'Bayesianreasoning_1')

<Axes: xlabel='isced', ylabel='Bayesianreasoning_1'>

# DF3 = Sorted by income

df3 = df2.sort_values(by = 'income')
df3 = df3.reset_index(drop = True)
df3.drop(df3[df3['income'] == 7].index, inplace = True)

df3

# Normalised mean of each facet as a numerical (sorted by income)

df3.groupby('income')[['Certainty_%','RiskComprehension_%','GraphLiteracy_%','Numeracy_%','Bayesianreasoning_%','TotalScore_%']].mean()

# Count of responses for each category (sorted by income)

df3.groupby('income')[['ResponseId']].count()

# Absolute mean of Total Facet score line plot (sorted by income)

df3.groupby('income')[['TotalScore_18']].mean().plot( kind = 'line', title = 'Absolute mean of Total Facet score line plot (sorted by income)').legend(loc='center left',bbox_to_anchor=(1.0, 0.5))

<matplotlib.legend.Legend at 0x27c36c29a90>

# Absolute mean of each Facet score line plot (sorted by income)

df3.groupby('income')[['Certainty_5', "Uncertainty_2", 'RiskComprehension_5','GraphLiteracy_3','Numeracy_2','Bayesianreasoning_1']].mean().plot( kind = 'line', title = 'Absolute Facet scores mean line plot').legend(loc='center left',bbox_to_anchor=(1.0, 0.5))

<matplotlib.legend.Legend at 0x27c36be80d0>

# Normalised mean of each Facet score line plot (sorted by income)


df3.groupby('income')[['Certainty_%', "Uncertainty_%", 'RiskComprehension_%','GraphLiteracy_%','Numeracy_%','Bayesianreasoning_%', 'TotalScore_%']].mean().plot( kind = 'line', title = '% mean of each Facet score line plot (sorted by income)').legend(loc='center left',bbox_to_anchor=(1.0, 0.5))

<matplotlib.legend.Legend at 0x27c36c2e190>

# Normalised mean of each Facet score stacked plot (sorted by income)

df3.groupby('income')[['Certainty_%', "Uncertainty_%", 'RiskComprehension_%','GraphLiteracy_%','Numeracy_%','Bayesianreasoning_%']].mean().plot( kind = 'bar', title = '% mean of each Facet score stacked plot (sorted by income)', stacked = True).legend(loc='center left',bbox_to_anchor=(1.0, 0.5))

<matplotlib.legend.Legend at 0x27c36765b10>

# Normalised mean of each Facet score hist plot (sorted by income)

df3.groupby('income')[['Certainty_%', "Uncertainty_%", 'RiskComprehension_%','GraphLiteracy_%','Numeracy_%','Bayesianreasoning_%','TotalScore_%']].mean().T.plot(kind = 'bar', title = '% mean scores for each facet for each income response category').legend(loc='center left',bbox_to_anchor=(1.0, 0.5))

<matplotlib.legend.Legend at 0x27c37806d50>

# Trend line for Absolute Total Facet Score vs income

sns.regplot (data = df3, x = 'income', y = 'TotalScore_18')

<Axes: xlabel='income', ylabel='TotalScore_18'>

# Trend line for Absolute Independent Facet Score vs income reponses

fig, ax = plt.subplots(figsize=(6, 6))

sns.regplot (data = df3, x = 'income', y = 'Certainty_5', fit_reg=True, ci=None, ax=ax, label='Certainty_5')
sns.regplot (data = df3, x = 'income', y = 'Uncertainty_2', fit_reg=True, ci=None, ax=ax, label='Uncertainty_2')
sns.regplot (data = df3, x = 'income', y = 'RiskComprehension_5', fit_reg=True, ci=None, ax=ax, label='RiskComprehension_5')
sns.regplot (data = df3, x = 'income', y = 'Numeracy_2', fit_reg=True, ci=None, ax=ax, label='Numeracy_2')
sns.regplot (data = df3, x = 'income', y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax, label='GraphLiteracy_3')
sns.regplot (data = df3, x = 'income', y = 'Bayesianreasoning_1',fit_reg=True, ci=None, ax=ax, label='Bayesianreasoning_1' )

ax.set(ylabel='Scores', xlabel='income')
ax.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

# Violin Plot for TotalScore_18 for each entry under income.

sns.violinplot( data = df3, x = 'income', y = 'TotalScore_18')

<Axes: xlabel='income', ylabel='TotalScore_18'>

# Violin Plot for Certainty_5 for each entry under income.

sns.violinplot( data = df3, x = 'income', y = 'Certainty_5')

<Axes: xlabel='income', ylabel='Certainty_5'>

# Violin Plot for Certainty_5 for each entry under income.

sns.violinplot( data = df3, x = 'income', y = 'Uncertainty_2')

<Axes: xlabel='income', ylabel='Uncertainty_2'>

# Violine Plot for RiskComprehension_5 for each entry under income.

sns.violinplot( data = df3, x = 'income', y = 'RiskComprehension_5')

<Axes: xlabel='income', ylabel='RiskComprehension_5'>

# Violine Plot for GraphLiteracy_3 for each entry under income.

sns.violinplot( data = df3, x = 'income', y = 'GraphLiteracy_3')

<Axes: xlabel='income', ylabel='GraphLiteracy_3'>

# Violine Plot for Numeracy_2 for each entry under income.

sns.violinplot( data = df3, x = 'income', y = 'Numeracy_2')

<Axes: xlabel='income', ylabel='Numeracy_2'>

# Violine Plot for TotalScore_18 for each entry under income.

sns.violinplot( data = df3, x = 'income', y = 'Bayesianreasoning_1')

<Axes: xlabel='income', ylabel='Bayesianreasoning_1'>

# Descriptive stats for the data set, isced = 1
# NA values of income are removed

df7 = df5
df7.drop(df7[df7['income'] == 7].index, inplace = True)

df7.loc[df7['isced']==1].describe()

df7.loc[df7['isced']==2].describe()

df7.loc[df7['isced']==3].describe()

# Trend line for Absolute Tota Facet Score vs income reponses sorted by isced and ORDERED by income WITH scatter

fig, ax7 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df7.loc[df7['isced']==1].reset_index(drop = True), x = 'income', y = df7.loc[df7['isced']==1]['TotalScore_18'], fit_reg=True, ci=None, ax=ax7, label='ISCED = 1')
sns.regplot (data = df7.loc[df7['isced']==2].reset_index(drop = True), x = 'income', y = df7.loc[df7['isced']==2]['TotalScore_18'], fit_reg=True, ci=None, ax=ax7, label='ISCED = 2')
sns.regplot (data = df7.loc[df7['isced']==3].reset_index(drop = True), x = 'income', y = df7.loc[df7['isced']==3]['TotalScore_18'], fit_reg=True, ci=None, ax=ax7, label='ISCED = 3')

ax7.set(ylabel='Total Scores_19', xlabel='INCOME')
ax7.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

# Trend line for Absolute Tota Facet Score vs income reponses sorted by isced and ORDERED by income WITHOUT scatter

fig, ax8 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[df7['isced']==1].reset_index(drop = True), x = 'income', y = 'TotalScore_18', fit_reg=True, ci=None, ax=ax8, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==2].reset_index(drop = True), x = 'income', y = 'TotalScore_18', fit_reg=True, ci=None, ax=ax8, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==3].reset_index(drop = True), x = 'income', y = 'TotalScore_18', fit_reg=True, ci=None, ax=ax8, label='ISCED = 3')

ax8.set(ylabel='Total Scores_19', xlabel='INCOME')
ax8.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

# Trend line for Absolute Certainty Score vs income reponses sorted by isced and ORDERED by income WITHOUT scatter

fig, ax9 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[df7['isced']==1].reset_index(drop = True), x = 'income', y = 'Certainty_5', fit_reg=True, ci=None, ax=ax9, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==2].reset_index(drop = True), x = 'income', y = 'Certainty_5', fit_reg=True, ci=None, ax=ax9, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==3].reset_index(drop = True), x = 'income', y = 'Certainty_5', fit_reg=True, ci=None, ax=ax9, label='ISCED = 3')

ax9.set(ylabel='Certainty_5', xlabel='INCOME')
ax9.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

# Trend line for Absolute Risk Comprehension Score vs income reponses sorted by isced and ORDERED by income WITHOUT scatter

fig, ax10 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[df7['isced']==1].reset_index(drop = True), x = 'income', y = 'RiskComprehension_5', fit_reg=True, ci=None, ax=ax10, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==2].reset_index(drop = True), x = 'income', y = 'RiskComprehension_5', fit_reg=True, ci=None, ax=ax10, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==3].reset_index(drop = True), x = 'income', y = 'RiskComprehension_5', fit_reg=True, ci=None, ax=ax10, label='ISCED = 3')

ax10.set(ylabel='RiskComprehension_5', xlabel='INCOME')
ax10.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

# Trend line for Absolute Number Comprehension Score vs income reponses sorted by isced and ORDERED by income WITHOUT scatter

fig, ax11 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[df7['isced']==1].reset_index(drop = True), x = 'income', y = 'Numeracy_2', fit_reg=True, ci=None, ax=ax11, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==2].reset_index(drop = True), x = 'income', y = 'Numeracy_2', fit_reg=True, ci=None, ax=ax11, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==3].reset_index(drop = True), x = 'income', y = 'Numeracy_2', fit_reg=True, ci=None, ax=ax11, label='ISCED = 3')

ax11.set(ylabel='Numeracy_2', xlabel='INCOME')
ax11.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

# Trend line for Absolute Graph Comprehension Score vs income reponses sorted by isced and ORDERED by income WITHOUT scatter

fig, ax11 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[df7['isced']==1].reset_index(drop = True), x = 'income', y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax11, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==2].reset_index(drop = True), x = 'income', y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax11, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==3].reset_index(drop = True), x = 'income', y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax11, label='ISCED = 3')

ax11.set(ylabel='GraphLiteracy_3', xlabel='INCOME')
ax11.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

# Trend line for Absolute Bayesian Reasoning Score vs income reponses sorted by isced and ORDERED by income WITHOUT scatter

fig, ax12 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[df7['isced']==1].reset_index(drop = True), x = 'income', y = 'Bayesianreasoning_1', fit_reg=True, ci=None, ax=ax12, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==2].reset_index(drop = True), x = 'income', y = 'Bayesianreasoning_1', fit_reg=True, ci=None, ax=ax12, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==3].reset_index(drop = True), x = 'income', y = 'Bayesianreasoning_1', fit_reg=True, ci=None, ax=ax12, label='ISCED = 3')

ax12.set(ylabel='Bayesianreasoning_1', xlabel='INCOME')
ax12.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

df7

# Since we already have a classification for Age groups in the form of age_rec, we will use that.
# We will also use median of age to see if it yields any relevant results, as instructed.

# AXES to be used = Age or age groups, ISCED, Income

# Age groups = [2,3,4]
# Age group 2 = 18 to 35 y/o
# Age group 3 = 36 to 59 y/o
# Age group 4 = 60 to 75 y/o (75 y/o, i.e, within the scope of the data we have, it can mean 60 and above also)

df7.loc[(df7['age_rec']==2)].describe()

df7.loc[(df7['age_rec']==3)].describe()

df7.loc[(df7['age_rec']==4)].describe()

fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (data = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (data = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='Total Scores_14', xlabel='INCOME for age 18 to 35')
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (data = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (data = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Total Scores_14', xlabel='INCOME for age 36 to 55')
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 1')
sns.regplot (data = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 2')
sns.regplot (data = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 3')

ax3.set(ylabel='Total Scores_14', xlabel='INCOME for age 56 and above')
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.errorbar(x = df7.loc[ df7['age_rec']==2  ]["income"].unique(), y = df7.loc[ df7['age_rec']==2  ].groupby(['income']).mean(numeric_only=True)['TotalScore_18'], yerr = df7.loc[ (df7['age_rec']==2) ].groupby(['income'])['TotalScore_18'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax1.set(ylabel='Total Scores_14', xlabel='INCOME for age 18 to 35',  yticks = np.arange(5, 16 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Total Scores_14', xlabel='INCOME for age 36 to 55',  yticks = np.arange(5, 16 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax2.errorbar(x = df7.loc[ df7['age_rec']==3  ]["income"].unique(), y = df7.loc[ df7['age_rec']==3  ].groupby(['income']).mean(numeric_only=True)['TotalScore_18'], yerr = df7.loc[ (df7['age_rec']==3) ].groupby(['income'])['TotalScore_18'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 3')

ax3.set(ylabel='Total Scores_14', xlabel='INCOME for age 56 and above',  yticks = np.arange(5, 16 , 1))
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax3.errorbar(x = df7.loc[ df7['age_rec']== 4 ]['income'].unique(), y = df7.loc[ df7['age_rec']==4  ].groupby(['income']).mean(numeric_only=True)['TotalScore_18'], yerr = df7.loc[ (df7['age_rec']==4) ].groupby(['income'])['TotalScore_18'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)]['Certainty_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)]['Certainty_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)]['Certainty_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='Certainty_5', xlabel='INCOME for age 18 to 35',  yticks = np.arange(0, 6 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax1.errorbar(x = df7.loc[ df7['age_rec']==2  ]["income"].unique(), y = df7.loc[ df7['age_rec']==2  ].groupby(['income'])['Certainty_5'].mean(), yerr = df7.loc[ (df7['age_rec']==2) ].groupby(['income'])['Certainty_5'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)]['Certainty_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)]['Certainty_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)]['Certainty_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Certainty_5', xlabel='INCOME for age 36 to 55',  yticks = np.arange(0, 6 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax2.errorbar(x = df7.loc[ df7['age_rec']==3  ]["income"].unique(), y = df7.loc[ df7['age_rec']==3  ].groupby(['income'])['Certainty_5'].mean(), yerr = df7.loc[ (df7['age_rec']==3) ].groupby(['income'])['Certainty_5'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)]['Certainty_5'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)]['Certainty_5'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)]['Certainty_5'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 3')

ax3.set(ylabel='Certainty_5', xlabel='INCOME for age 56 and above',  yticks = np.arange(0, 6 , 1))
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax3.errorbar(x = df7.loc[ df7['age_rec']== 4 ]['income'].unique(), y = df7.loc[ df7['age_rec']==4  ].groupby(['income'])['Certainty_5'].mean(), yerr = df7.loc[ (df7['age_rec']==4) ].groupby(['income'])['Certainty_5'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='RiskComprehension_5', xlabel='INCOME for age 18 to 35',  yticks = np.arange(0, 3 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax1.errorbar(x = df7.loc[ df7['age_rec']==2  ]["income"].unique(), y = df7.loc[ df7['age_rec']==2  ].groupby(['income'])['RiskComprehension_5'].mean(), yerr = df7.loc[ (df7['age_rec']==2) ].groupby(['income'])['RiskComprehension_5'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='RiskComprehension_5', xlabel='INCOME for age 36 to 55',  yticks = np.arange(0, 3 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax2.errorbar(x = df7.loc[ df7['age_rec']==3  ]["income"].unique(), y = df7.loc[ df7['age_rec']==3  ].groupby(['income'])['RiskComprehension_5'].mean(), yerr = df7.loc[ (df7['age_rec']==3) ].groupby(['income'])['RiskComprehension_5'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 3')

ax3.set(ylabel='RiskComprehension_5', xlabel='INCOME for age 56 and above',  yticks = np.arange(0, 3 , 1))
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax3.errorbar(x = df7.loc[ df7['age_rec']== 4 ]['income'].unique(), y = df7.loc[ df7['age_rec']==4  ].groupby(['income'])['RiskComprehension_5'].mean(), yerr = df7.loc[ (df7['age_rec']==4) ].groupby(['income'])['RiskComprehension_5'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='Numeracy_2', xlabel='INCOME for age 18 to 35',  yticks = np.arange(0, 6 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax1.errorbar(x = df7.loc[ df7['age_rec']==2  ]["income"].unique(), y = df7.loc[ df7['age_rec']==2  ].groupby(['income'])['Numeracy_2'].mean(), yerr = df7.loc[ (df7['age_rec']==2) ].groupby(['income'])['Numeracy_2'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Numeracy_2', xlabel='INCOME for age 36 to 55',  yticks = np.arange(0, 6 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax2.errorbar(x = df7.loc[ df7['age_rec']==3  ]["income"].unique(), y = df7.loc[ df7['age_rec']==3  ].groupby(['income'])['Numeracy_2'].mean(), yerr = df7.loc[ (df7['age_rec']==3) ].groupby(['income'])['Numeracy_2'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 3')

ax3.set(ylabel='Numeracy_2', xlabel='INCOME for age 56 and above',  yticks = np.arange(0, 6 , 1))
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax3.errorbar(x = df7.loc[ df7['age_rec']== 4 ]['income'].unique(), y = df7.loc[ df7['age_rec']==4  ].groupby(['income'])['Numeracy_2'].mean(), yerr = df7.loc[ (df7['age_rec']==4) ].groupby(['income'])['Numeracy_2'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='GraphLiteracy_3', xlabel='INCOME for age 18 to 35',  yticks = np.arange(0, 4 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax1.errorbar(x = df7.loc[ df7['age_rec']==2  ]["income"].unique(), y = df7.loc[ df7['age_rec']==2  ].groupby(['income'])['GraphLiteracy_3'].mean(), yerr = df7.loc[ (df7['age_rec']==2) ].groupby(['income'])['GraphLiteracy_3'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='GraphLiteracy_3', xlabel='INCOME for age 36 to 55',  yticks = np.arange(0, 4 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax2.errorbar(x = df7.loc[ df7['age_rec']==3  ]["income"].unique(), y = df7.loc[ df7['age_rec']==3  ].groupby(['income'])['GraphLiteracy_3'].mean(), yerr = df7.loc[ (df7['age_rec']==3) ].groupby(['income'])['GraphLiteracy_3'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 3')

ax3.set(ylabel='GraphLiteracy_3', xlabel='INCOME for age 56 and above',  yticks = np.arange(0, 4 , 1))
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax3.errorbar(x = df7.loc[ df7['age_rec']== 4 ]['income'].unique(), y = df7.loc[ df7['age_rec']==4  ].groupby(['income'])['GraphLiteracy_3'].mean(), yerr = df7.loc[ (df7['age_rec']==4) ].groupby(['income'])['GraphLiteracy_3'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='Bayesianreasoning_1', xlabel='INCOME for age 18 to 35',  yticks = np.arange(0, 5 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax1.errorbar(x = df7.loc[ df7['age_rec']==2  ]["income"].unique(), y = df7.loc[ df7['age_rec']==2  ].groupby(['income'])['Bayesianreasoning_1'].mean(), yerr = df7.loc[ (df7['age_rec']==2) ].groupby(['income'])['Bayesianreasoning_1'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Bayesianreasoning_1', xlabel='INCOME for age 36 to ',  yticks = np.arange(0, 5 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax2.errorbar(x = df7.loc[ df7['age_rec']==3  ]["income"].unique(), y = df7.loc[ df7['age_rec']==3  ].groupby(['income'])['Bayesianreasoning_1'].mean(), yerr = df7.loc[ (df7['age_rec']==3) ].groupby(['income'])['Bayesianreasoning_1'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 3')

ax3.set(ylabel='Bayesianreasoning_1', xlabel='INCOME for age 56 and above',  yticks = np.arange(0, 5 , 1))
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax3.errorbar(x = df7.loc[ df7['age_rec']== 4 ]['income'].unique(), y = df7.loc[ df7['age_rec']==4  ].groupby(['income'])['Bayesianreasoning_1'].mean(), yerr = df7.loc[ (df7['age_rec']==4) ].groupby(['income'])['Bayesianreasoning_1'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

# Age mean method
# Age_mean

age_m = df2["age"].median()
age_m

30.5

df7["income"].value_counts()

income
2    371
1    250
3    148
4     93
5     44
Name: count, dtype: int64

df7.loc[(df7['age'] < age_m)].describe()

df7.loc[(df7['age'] >= age_m)].describe()

fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (data = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (data = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='Total Scores_19', xlabel='INCOME for < median age')
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df7.loc[(df7['age'] >= age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==1)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (data = df7.loc[(df7['age'] >=age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==2)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (data = df7.loc[(df7['age'] >=age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==3)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Total Scores_19', xlabel='INCOME for >=median age')
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.errorbar(x = df7.loc[ (df7['age'] < age_m)  ]["income"].unique(), y = df7.loc[ (df7['age'] < age_m)  ].groupby(['income'])['TotalScore_18'].mean(), yerr = df7.loc[ (df7['age'] < age_m) ].groupby(['income'])['TotalScore_18'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax1.set(ylabel='Total Scores_19', xlabel='INCOME for < median age',  yticks = np.arange(5, 16 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] >=age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==1)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] >=age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==2)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] >=age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==3)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.errorbar(x = df7.loc[ (df7['age'] >= age_m)  ]["income"].unique(), y = df7.loc[ (df7['age'] >=age_m)  ].groupby(['income'])['TotalScore_18'].mean(), yerr = df7.loc[ (df7['age'] >=age_m) ].groupby(['income'])['TotalScore_18'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax2.set(ylabel='Total Scores_19', xlabel='INCOME for >=median age',  yticks = np.arange(5, 16 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)]['Certainty_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)]['Certainty_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)]['Certainty_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.errorbar(x = df7.loc[ (df7['age'] < age_m)  ]["income"].unique(), y = df7.loc[ (df7['age'] < age_m)  ].groupby(['income'])['Certainty_5'].mean(), yerr = df7.loc[ (df7['age'] < age_m) ].groupby(['income'])['Certainty_5'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax1.set(ylabel='Certainty_5', xlabel='INCOME for < median age',  yticks = np.arange(0, 6 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] >=age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==1)]['Certainty_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] >=age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==2)]['Certainty_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] >=age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==3)]['Certainty_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.errorbar(x = df7.loc[ (df7['age'] >= age_m)  ]["income"].unique(), y = df7.loc[ (df7['age'] >=age_m)  ].groupby(['income'])['Certainty_5'].mean(), yerr = df7.loc[ (df7['age'] >=age_m) ].groupby(['income'])['Certainty_5'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax2.set(ylabel='Certainty_5', xlabel='INCOME for >=median age')
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.errorbar(x = df7.loc[ (df7['age'] < age_m)  ]["income"].unique(), y = df7.loc[ (df7['age'] < age_m)  ].groupby(['income'])['RiskComprehension_5'].mean(), yerr = df7.loc[ (df7['age'] < age_m) ].groupby(['income'])['RiskComprehension_5'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax1.set(ylabel='RiskComprehension_5', xlabel='INCOME for < median age',  yticks = np.arange(0, 3 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] >=age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==1)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] >=age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==2)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] >=age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==3)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.errorbar(x = df7.loc[ (df7['age'] >= age_m)  ]["income"].unique(), y = df7.loc[ (df7['age'] >=age_m)  ].groupby(['income'])['RiskComprehension_5'].mean(), yerr = df7.loc[ (df7['age'] >=age_m) ].groupby(['income'])['RiskComprehension_5'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax2.set(ylabel='RiskComprehension_5', xlabel='INCOME for >=median age',  yticks = np.arange(0, 3 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.errorbar(x = df7.loc[ (df7['age'] < age_m)  ]["income"].unique(), y = df7.loc[ (df7['age'] < age_m)  ].groupby(['income'])['Numeracy_2'].mean(), yerr = df7.loc[ (df7['age'] < age_m) ].groupby(['income'])['Numeracy_2'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax1.set(ylabel='Numeracy_2', xlabel='INCOME for < median age',  yticks = np.arange(0, 6 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] >=age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==1)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] >=age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==2)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] >=age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==3)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.errorbar(x = df7.loc[ (df7['age'] >= age_m)  ]["income"].unique(), y = df7.loc[ (df7['age'] >=age_m)  ].groupby(['income'])['Numeracy_2'].mean(), yerr = df7.loc[ (df7['age'] >=age_m) ].groupby(['income'])['Numeracy_2'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax2.set(ylabel='Numeracy_2', xlabel='INCOME for >=median age',  yticks = np.arange(0, 6 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.errorbar(x = df7.loc[ (df7['age'] < age_m)  ]["income"].unique(), y = df7.loc[ (df7['age'] < age_m)  ].groupby(['income'])['GraphLiteracy_3'].mean(), yerr = df7.loc[ (df7['age'] < age_m) ].groupby(['income'])['GraphLiteracy_3'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax1.set(ylabel='GraphLiteracy_3', xlabel='INCOME for < median age',  yticks = np.arange(0, 4 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] >=age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==1)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] >=age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==2)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] >=age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==3)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.errorbar(x = df7.loc[ (df7['age'] >= age_m)  ]["income"].unique(), y = df7.loc[ (df7['age'] >=age_m)  ].groupby(['income'])['GraphLiteracy_3'].mean(), yerr = df7.loc[ (df7['age'] >=age_m) ].groupby(['income'])['GraphLiteracy_3'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax2.set(ylabel='GraphLiteracy_3', xlabel='INCOME for >=median age',  yticks = np.arange(0, 4 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.errorbar(x = df7.loc[ (df7['age'] < age_m)  ]["income"].unique(), y = df7.loc[ (df7['age'] < age_m)  ].groupby(['income'])['Bayesianreasoning_1'].mean(), yerr = df7.loc[ (df7['age'] < age_m) ].groupby(['income'])['Bayesianreasoning_1'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax1.set(ylabel='Bayesianreasoning_1', xlabel='INCOME for < median age',  yticks = np.arange(0, 5 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] >=age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==1)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] >=age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==2)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] >=age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] >=age_m) & (df7['isced']==3)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.errorbar(x = df7.loc[ (df7['age'] >= age_m)  ]["income"].unique(), y = df7.loc[ (df7['age'] >=age_m)  ].groupby(['income'])['Bayesianreasoning_1'].mean(), yerr = df7.loc[ (df7['age'] >=age_m) ].groupby(['income'])['Bayesianreasoning_1'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax2.set(ylabel='Bayesianreasoning_1', xlabel='INCOME for >=median age',  yticks = np.arange(0, 5 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

# Data Frame 2 or df2 is an aggregate data on facet total scores and over all total score, along with wealth and income data vs each response. 
# This data set is also sorted by wealth with all responses 'NA' removed.
# We also assign quartiles ranking each response according to this sort.

df4 = df2.sort_values(by = 'wealth')
df4 = df4.reset_index(drop = True)
df4 = df4.dropna(axis = 0, subset = 'wealth')
df4.insert(loc = len(df4.columns), column = "Quartile Number", value = pd.qcut(df4["wealth"],q = 4, labels = False ) + 1, allow_duplicates = 'False')

df4

# Trend line for Absolute Total Facet Score vs unique wealth reponses

sns.regplot (data = df4, x = df4.index, y = 'TotalScore_18')

<Axes: ylabel='TotalScore_18'>

# Trend line for Absolute Independent Facet Score vs wealth reponses

fig1, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df4, x = df4.index, y = 'Certainty_5', fit_reg=True, ci=None, ax=ax1, label='Certainty_5')
sns.regplot (data = df4, x = df4.index, y = 'RiskComprehension_5', fit_reg=True, ci=None, ax=ax1, label='RiskComprehension_5')
sns.regplot (data = df4, x = df4.index, y = 'Numeracy_2', fit_reg=True, ci=None, ax=ax1, label='Numeracy_2')
sns.regplot (data = df4, x = df4.index, y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax1, label='GraphLiteracy_3')
sns.regplot (data = df4, x = df4.index, y = 'Bayesianreasoning_1',fit_reg=True, ci=None, ax=ax1, label='Bayesianreasoning_1' )

ax1.set(ylabel='Scores', xlabel='wealth')
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

# Count of number of responses under each quartile

df4.groupby('Quartile Number')[['Quartile Number']].count()

# Absolute Total Facet scores mean line plot from low to high Wealth

df4.groupby('Quartile Number')[['TotalScore_18']].mean().plot( kind = 'line', title = 'Absolute Total Facet scores mean line plot low to high wealth', xticks = np.arange(1,5,step = 1)).legend(loc='center left',bbox_to_anchor=(1.0, 0.5))

<matplotlib.legend.Legend at 0x27c38b415d0>

# Absolute Facet scores mean line plot from low to high wealth

df4.groupby('Quartile Number')[['Certainty_5', "Uncertainty_2", 'RiskComprehension_5','Numeracy_2','GraphLiteracy_3','Bayesianreasoning_1']].mean().plot( kind = 'line', title = 'Absolute Facet scores mean line plot low to high wealth', xticks = np.arange(1,5,step = 1)).legend(loc='center left',bbox_to_anchor=(1.0, 0.5))

<matplotlib.legend.Legend at 0x27c38c07cd0>

# Normalised Facet scores mean line plot from low to high wealth


df4.groupby('Quartile Number')[['Certainty_%', "Uncertainty_%", 'RiskComprehension_%','Numeracy_%','GraphLiteracy_%','Bayesianreasoning_%','TotalScore_%']].mean().plot( kind = 'line', title = '% Facet scores mean line plot from low to high wealth',  xticks = np.arange(1,5,step = 1)).legend(loc='center left',bbox_to_anchor=(1.0, 0.5))

<matplotlib.legend.Legend at 0x27c38ed2b90>

# Normalised mean scores for each facet stacked

df4.groupby('Quartile Number')[['Certainty_%', "Uncertainty_%", 'RiskComprehension_%','Numeracy_%','GraphLiteracy_%','Bayesianreasoning_%']].mean().plot( kind = 'bar', title = '% mean scores for each facet stacked', stacked = True).legend(loc='center left',bbox_to_anchor=(1.0, 0.5))

<matplotlib.legend.Legend at 0x27c399e3f90>

# Normalised mean scores for each facet for each wealth response Quartile

df4.groupby('Quartile Number')[['Certainty_%', "Uncertainty_%", 'RiskComprehension_%','Numeracy_%','GraphLiteracy_%','Bayesianreasoning_%','TotalScore_%']].mean().T.plot(kind = 'bar', title = '% mean scores for each facet for each wealth response category').legend(loc='center left',bbox_to_anchor=(1.0, 0.5))

<matplotlib.legend.Legend at 0x27c30177e10>

# Trend line for Absolute Total Facet Score vs Quartiles

sns.regplot (data = df4, x = 'Quartile Number', y = 'TotalScore_18')

<Axes: xlabel='Quartile Number', ylabel='TotalScore_18'>

# Trend line for Absolute Independent Facet Score vs Quartiles

fig2, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df4, x = 'Quartile Number', y = 'Certainty_5', fit_reg=True, ci=None, ax=ax2, label='Certainty_5')
sns.regplot (data = df4, x = 'Quartile Number', y = 'Uncertainty_2', fit_reg=True, ci=None, ax=ax2, label='Uncertainty_2')
sns.regplot (data = df4, x = 'Quartile Number', y = 'RiskComprehension_5', fit_reg=True, ci=None, ax=ax2, label='RiskComprehension_5')
sns.regplot (data = df4, x = 'Quartile Number', y = 'Numeracy_2', fit_reg=True, ci=None, ax=ax2, label='Numeracy_2')
sns.regplot (data = df4, x = 'Quartile Number', y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax2, label='GraphLiteracy_3')
sns.regplot (data = df4, x = 'Quartile Number', y = 'Bayesianreasoning_1',fit_reg=True, ci=None, ax=ax2, label='Bayesianreasoning_1' )

ax2.set(ylabel='Scores', xlabel='wealth')
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

# Violine Plot for TotalScore_18 for each entry under Wealth Quartiles.

sns.violinplot( data = df4, x = 'Quartile Number', y = 'TotalScore_18')

<Axes: xlabel='Quartile Number', ylabel='TotalScore_18'>

# Violine Plot for Certainty_5 for each entry under Wealth Quartiles.

sns.violinplot( data = df4, x = 'Quartile Number', y = 'Certainty_5')

<Axes: xlabel='Quartile Number', ylabel='Certainty_5'>

# Violine Plot for Certainty_5 for each entry under Wealth Quartiles.

sns.violinplot( data = df4, x = 'Quartile Number', y = 'Uncertainty_2')

<Axes: xlabel='Quartile Number', ylabel='Uncertainty_2'>

# Violine Plot for RiskComprehension_5 for each entry under Wealth Quartiles.

sns.violinplot( data = df4, x = 'Quartile Number', y = 'RiskComprehension_5')

<Axes: xlabel='Quartile Number', ylabel='RiskComprehension_5'>

# Violine Plot for GraphLiteracy_3 for each entry under Wealth Quartiles.

sns.violinplot( data = df4, x = 'Quartile Number', y = 'GraphLiteracy_3')

<Axes: xlabel='Quartile Number', ylabel='GraphLiteracy_3'>

# Violine Plot for Numeracy_2 for each entry under Wealth Quartiles.

sns.violinplot( data = df4, x = 'Quartile Number', y = 'Numeracy_2')

<Axes: xlabel='Quartile Number', ylabel='Numeracy_2'>

# Violine Plot for Bayesianreasoning_1 for each entry under Wealth Quartiles.

sns.violinplot( data = df4, x = 'Quartile Number', y = 'Bayesianreasoning_1')

<Axes: xlabel='Quartile Number', ylabel='Bayesianreasoning_1'>

# Descriptive stats for the data set, isced = 1
# NA values of wealth are removed

df6 = df5.dropna(axis = 0, subset = 'wealth')
# df6.drop(df6[df6['wealth'] == 3500000].index, inplace = True)
df6.loc[df6['isced']==1][['wealth']].describe()

# Descriptive stats for the data set, isced = 2

df6.loc[df6['isced']==2][['wealth']].describe()

# Descriptive stats for the data set, isced = 3

df6.loc[df6['isced']==3][['wealth']].describe()

# Trend line for Absolute Tota Facet Score vs wealth reponses sorted by isced and ORDERED by wealth WITH scatter

fig, ax7 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df6.loc[df6['isced']==1].reset_index(drop = True), x = df6.loc[df6['isced']==1].reset_index(drop = True)['wealth'], y = 'TotalScore_18', fit_reg=True, ci=None, ax=ax7, label='ISCED = 1')
sns.regplot (data = df6.loc[df6['isced']==2].reset_index(drop = True), x = df6.loc[df6['isced']==2].reset_index(drop = True)['wealth'], y = 'TotalScore_18', fit_reg=True, ci=None, ax=ax7, label='ISCED = 2')
sns.regplot (data = df6.loc[df6['isced']==3].reset_index(drop = True), x = df6.loc[df6['isced']==3].reset_index(drop = True)['wealth'], y = 'TotalScore_18', fit_reg=True, ci=None, ax=ax7, label='ISCED = 3')

ax7.set(ylabel='Total Scores_19', xlabel='Wealth')
ax7.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

# Trend line for Absolute Tota Facet Score vs wealth reponses sorted by isced and ORDERED by wealth WITHOUT scatter

fig, ax8 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df6.loc[df6['isced']==1].reset_index(drop = True), x = df6.loc[df6['isced']==1].reset_index(drop = True)['wealth'], y = 'TotalScore_18', fit_reg=True, ci=None, ax=ax8, label='ISCED = 1')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==2].reset_index(drop = True), x = df6.loc[df6['isced']==2].reset_index(drop = True)['wealth'], y = 'TotalScore_18', fit_reg=True, ci=None, ax=ax8, label='ISCED = 2')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==3].reset_index(drop = True), x = df6.loc[df6['isced']==3].reset_index(drop = True)['wealth'], y = 'TotalScore_18', fit_reg=True, ci=None, ax=ax8, label='ISCED = 3')

ax8.set(ylabel='Total Scores_19', xlabel='Wealth')
ax8.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

# Trend line for Absolute Certainty Score vs wealth reponses sorted by isced and ORDERED by wealth WITHOUT scatter

fig, ax9 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df6.loc[df6['isced']==1].reset_index(drop = True), x = df6.loc[df6['isced']==1].reset_index(drop = True)['wealth'], y = 'Certainty_5', fit_reg=True, ci=None, ax=ax9, label='ISCED = 1')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==2].reset_index(drop = True), x = df6.loc[df6['isced']==2].reset_index(drop = True)['wealth'], y = 'Certainty_5', fit_reg=True, ci=None, ax=ax9, label='ISCED = 2')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==3].reset_index(drop = True), x = df6.loc[df6['isced']==3].reset_index(drop = True)['wealth'], y = 'Certainty_5', fit_reg=True, ci=None, ax=ax9, label='ISCED = 3')

ax9.set(ylabel='Certainty_5', xlabel='Wealth')
ax9.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

# Trend line for Absolute Uncertainty Score vs wealth reponses sorted by isced and ORDERED by wealth WITHOUT scatter

fig, ax10 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df6.loc[df6['isced']==1].reset_index(drop = True), x = df6.loc[df6['isced']==1].reset_index(drop = True)['wealth'], y = 'RiskComprehension_5', fit_reg=True, ci=None, ax=ax10, label='ISCED = 1')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==2].reset_index(drop = True), x = df6.loc[df6['isced']==2].reset_index(drop = True)['wealth'], y = 'RiskComprehension_5', fit_reg=True, ci=None, ax=ax10, label='ISCED = 2')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==3].reset_index(drop = True), x = df6.loc[df6['isced']==3].reset_index(drop = True)['wealth'], y = 'RiskComprehension_5', fit_reg=True, ci=None, ax=ax10, label='ISCED = 3')

ax10.set(ylabel='RiskComprehension_5', xlabel='Wealth')
ax10.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

# Trend line for Absolute Number Comprehension Score vs wealth reponses sorted by isced and ORDERED by wealth WITHOUT scatter

fig, ax11 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df6.loc[df6['isced']==1].reset_index(drop = True), x = df6.loc[df6['isced']==1].reset_index(drop = True)['wealth'], y = 'Numeracy_2', fit_reg=True, ci=None, ax=ax11, label='ISCED = 1')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==2].reset_index(drop = True), x = df6.loc[df6['isced']==2].reset_index(drop = True)['wealth'], y = 'Numeracy_2', fit_reg=True, ci=None, ax=ax11, label='ISCED = 2')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==3].reset_index(drop = True), x = df6.loc[df6['isced']==3].reset_index(drop = True)['wealth'], y = 'Numeracy_2', fit_reg=True, ci=None, ax=ax11, label='ISCED = 3')

ax11.set(ylabel='Numeracy_2', xlabel='Wealth')
ax11.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

# Trend line for Absolute Graph Comprehension Score vs wealth reponses sorted by isced and ORDERED by wealth WITHOUT scatter

fig, ax11 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df6.loc[df6['isced']==1].reset_index(drop = True), x = df6.loc[df6['isced']==1].reset_index(drop = True)['wealth'], y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax11, label='ISCED = 1')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==2].reset_index(drop = True), x = df6.loc[df6['isced']==2].reset_index(drop = True)['wealth'], y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax11, label='ISCED = 2')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==3].reset_index(drop = True), x = df6.loc[df6['isced']==3].reset_index(drop = True)['wealth'], y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax11, label='ISCED = 3')

ax11.set(ylabel='GraphLiteracy_3', xlabel='Wealth')
ax11.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

# Trend line for Absolute Bayesian Reasoning Score vs wealth reponses sorted by isced and ORDERED by wealth WITHOUT scatter

fig, ax12 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df6.loc[df6['isced']==1].reset_index(drop = True), x = df6.loc[df6['isced']==1].reset_index(drop = True)['wealth'], y = 'Bayesianreasoning_1', fit_reg=True, ci=None, ax=ax12, label='ISCED = 1')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==2].reset_index(drop = True), x = df6.loc[df6['isced']==2].reset_index(drop = True)['wealth'], y = 'Bayesianreasoning_1', fit_reg=True, ci=None, ax=ax12, label='ISCED = 2')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==3].reset_index(drop = True), x = df6.loc[df6['isced']==3].reset_index(drop = True)['wealth'], y = 'Bayesianreasoning_1', fit_reg=True, ci=None, ax=ax12, label='ISCED = 3')

ax12.set(ylabel='Bayesianreasoning_1', xlabel='Wealth')
ax12.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

# Descriptive stats for the data set, isced = 1
# NA values of wealth are removed

df4.loc[df4['isced']==1].describe()

df4.loc[df4['isced']==2].describe()

df4.loc[df4['isced']==3].describe()

# Trend line for Absolute Tota Facet Score vs Quartile Number reponses sorted by isced and ORDERED by Quartile Number WITH scatter

fig, ax7 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df4.loc[df4['isced']==1].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[df4['isced']==1]['TotalScore_18'], fit_reg=True, ci=None, ax=ax7, label='ISCED = 1')
sns.regplot (data = df4.loc[df4['isced']==2].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[df4['isced']==2]['TotalScore_18'], fit_reg=True, ci=None, ax=ax7, label='ISCED = 2')
sns.regplot (data = df4.loc[df4['isced']==3].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[df4['isced']==3]['TotalScore_18'], fit_reg=True, ci=None, ax=ax7, label='ISCED = 3')

ax7.set(ylabel='Total Scores_19', xlabel='WEALTH Quartile Number')
ax7.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

# Trend line for Absolute Tota Facet Score vs Quartile Number reponses sorted by isced and ORDERED by Quartile Number WITHOUT scatter

fig, ax8 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[df4['isced']==1].reset_index(drop = True), x = 'Quartile Number', y = 'TotalScore_18', fit_reg=True, ci=None, ax=ax8, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[df4['isced']==2].reset_index(drop = True), x = 'Quartile Number', y = 'TotalScore_18', fit_reg=True, ci=None, ax=ax8, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[df4['isced']==3].reset_index(drop = True), x = 'Quartile Number', y = 'TotalScore_18', fit_reg=True, ci=None, ax=ax8, label='ISCED = 3')

ax8.set(ylabel='Total Scores_19', xlabel='WEALTH Quartile Number')
ax8.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

# Trend line for Absolute Certainty Score vs Quartile Number reponses sorted by isced and ORDERED by Quartile Number WITHOUT scatter

fig, ax9 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[df4['isced']==1].reset_index(drop = True), x = 'Quartile Number', y = 'Certainty_5', fit_reg=True, ci=None, ax=ax9, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[df4['isced']==2].reset_index(drop = True), x = 'Quartile Number', y = 'Certainty_5', fit_reg=True, ci=None, ax=ax9, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[df4['isced']==3].reset_index(drop = True), x = 'Quartile Number', y = 'Certainty_5', fit_reg=True, ci=None, ax=ax9, label='ISCED = 3')

ax9.set(ylabel='Certainty_5', xlabel='WEALTH Quartile Number')
ax9.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

# Trend line for Absolute Risk Comprehension Score vs Quartile Number reponses sorted by isced and ORDERED by Quartile Number WITHOUT scatter

fig, ax10 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[df4['isced']==1].reset_index(drop = True), x = 'Quartile Number', y = 'RiskComprehension_5', fit_reg=True, ci=None, ax=ax10, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[df4['isced']==2].reset_index(drop = True), x = 'Quartile Number', y = 'RiskComprehension_5', fit_reg=True, ci=None, ax=ax10, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[df4['isced']==3].reset_index(drop = True), x = 'Quartile Number', y = 'RiskComprehension_5', fit_reg=True, ci=None, ax=ax10, label='ISCED = 3')

ax10.set(ylabel='RiskComprehension_5', xlabel='WEALTH Quartile Number')
ax10.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

# Trend line for Absolute Number Comprehension Score vs Quartile Number reponses sorted by isced and ORDERED by Quartile Number WITHOUT scatter

fig, ax11 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[df4['isced']==1].reset_index(drop = True), x = 'Quartile Number', y = 'Numeracy_2', fit_reg=True, ci=None, ax=ax11, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[df4['isced']==2].reset_index(drop = True), x = 'Quartile Number', y = 'Numeracy_2', fit_reg=True, ci=None, ax=ax11, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[df4['isced']==3].reset_index(drop = True), x = 'Quartile Number', y = 'Numeracy_2', fit_reg=True, ci=None, ax=ax11, label='ISCED = 3')

ax11.set(ylabel='Numeracy_2', xlabel='WEALTH Quartile Number')
ax11.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

# Trend line for Absolute Graph Comprehension Score vs Quartile Number reponses sorted by isced and ORDERED by Quartile Number WITHOUT scatter

fig, ax11 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[df4['isced']==1].reset_index(drop = True), x = 'Quartile Number', y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax11, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[df4['isced']==2].reset_index(drop = True), x = 'Quartile Number', y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax11, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[df4['isced']==3].reset_index(drop = True), x = 'Quartile Number', y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax11, label='ISCED = 3')

ax11.set(ylabel='GraphLiteracy_3', xlabel='WEALTH Quartile Number')
ax11.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

# Trend line for Absolute Bayesian Reasoning Score vs Quartile Number reponses sorted by isced and ORDERED by Quartile Number WITHOUT scatter

fig, ax12 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[df4['isced']==1].reset_index(drop = True), x = 'Quartile Number', y = 'Bayesianreasoning_1', fit_reg=True, ci=None, ax=ax12, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[df4['isced']==2].reset_index(drop = True), x = 'Quartile Number', y = 'Bayesianreasoning_1', fit_reg=True, ci=None, ax=ax12, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[df4['isced']==3].reset_index(drop = True), x = 'Quartile Number', y = 'Bayesianreasoning_1', fit_reg=True, ci=None, ax=ax12, label='ISCED = 3')

ax12.set(ylabel='Bayesianreasoning_1', xlabel='WEALTH Quartile Number')
ax12.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

df4

# Since we already have a classification for Age groups in the form of age_rec, we will use that.
# We will also use median of age to see if it yields any relevant results, as instructed.

# AXES to be used = Age or age groups, ISCED, Income

# Age groups = [2,3,4]
# Age group 2 = 18 to 35 y/o
# Age group 3 = 36 to 59 y/o
# Age group 4 = 60 to 75 y/o (75 y/o, i.e, within the scope of the data we have, it can mean 60 and above also)

df4.loc[(df4['age_rec']==2)].describe()

df4.loc[(df4['age_rec']==3)].describe()

df4.loc[(df4['age_rec']==4)].describe()

fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df4.loc[(df4['age_rec']==2) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==2) & (df4['isced']==1)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (data = df4.loc[(df4['age_rec']==2) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==2) & (df4['isced']==2)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (data = df4.loc[(df4['age_rec']==2) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==2) & (df4['isced']==3)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='Total Scores_14', xlabel='WEALTH Quartile Number for age 18 to 35')
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df4.loc[(df4['age_rec']==3) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==3) & (df4['isced']==1)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (data = df4.loc[(df4['age_rec']==3) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==3) & (df4['isced']==2)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (data = df4.loc[(df4['age_rec']==3) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==3) & (df4['isced']==3)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Total Scores_14', xlabel='WEALTH Quartile Number for age 36 to 55')
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df4.loc[(df4['age_rec']==4) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==4) & (df4['isced']==1)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 1')
sns.regplot (data = df4.loc[(df4['age_rec']==4) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==4) & (df4['isced']==2)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 2')
sns.regplot (data = df4.loc[(df4['age_rec']==4) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==4) & (df4['isced']==3)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 3')

ax3.set(ylabel='Total Scores_14', xlabel='WEALTH Quartile Number for age 56 and above')
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==2) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==2) & (df4['isced']==1)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==2) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==2) & (df4['isced']==2)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==2) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==2) & (df4['isced']==3)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.errorbar(x = df4.loc[ df4['age_rec']==2  ]["Quartile Number"].unique(), y = df4.loc[ df4['age_rec']==2  ].groupby(['Quartile Number']).mean(numeric_only=True)['TotalScore_18'], yerr = df4.loc[ (df4['age_rec']==2) ].groupby(['Quartile Number'])['TotalScore_18'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax1.set(ylabel='Total Scores_14', xlabel='WEALTH Quartile Number for age 18 to 35',  yticks = np.arange(5, 16 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==3) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==3) & (df4['isced']==1)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==3) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==3) & (df4['isced']==2)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==3) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==3) & (df4['isced']==3)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Total Scores_14', xlabel='WEALTH Quartile Number for age 36 to 55',  yticks = np.arange(5, 16 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax2.errorbar(x = df4.loc[ df4['age_rec']==3  ]["Quartile Number"].unique(), y = df4.loc[ df4['age_rec']==3  ].groupby(['Quartile Number']).mean(numeric_only=True)['TotalScore_18'], yerr = df4.loc[ (df4['age_rec']==3) ].groupby(['Quartile Number'])['TotalScore_18'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==4) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==4) & (df4['isced']==1)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==4) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==4) & (df4['isced']==2)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==4) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==4) & (df4['isced']==3)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 3')

ax3.set(ylabel='Total Scores_14', xlabel='WEALTH Quartile Number for age 56 and above',  yticks = np.arange(5, 16 , 1))
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax3.errorbar(x = df4.loc[ df4['age_rec']== 4 ]['Quartile Number'].unique(), y = df4.loc[ df4['age_rec']==4  ].groupby(['Quartile Number']).mean(numeric_only=True)['TotalScore_18'], yerr = df4.loc[ (df4['age_rec']==4) ].groupby(['Quartile Number'])['TotalScore_18'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==2) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==2) & (df4['isced']==1)]['Certainty_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==2) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==2) & (df4['isced']==2)]['Certainty_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==2) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==2) & (df4['isced']==3)]['Certainty_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='Certainty_5', xlabel='WEALTH Quartile Number for age 18 to 35',  yticks = np.arange(0, 6 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax1.errorbar(x = df4.loc[ df4['age_rec']==2  ]["Quartile Number"].unique(), y = df4.loc[ df4['age_rec']==2  ].groupby(['Quartile Number'])['Certainty_5'].mean(), yerr = df4.loc[ (df4['age_rec']==2) ].groupby(['Quartile Number'])['Certainty_5'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==3) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==3) & (df4['isced']==1)]['Certainty_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==3) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==3) & (df4['isced']==2)]['Certainty_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==3) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==3) & (df4['isced']==3)]['Certainty_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Certainty_5', xlabel='WEALTH Quartile Number for age 36 to 55',  yticks = np.arange(0, 6 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax2.errorbar(x = df4.loc[ df4['age_rec']==3  ]["Quartile Number"].unique(), y = df4.loc[ df4['age_rec']==3  ].groupby(['Quartile Number'])['Certainty_5'].mean(), yerr = df4.loc[ (df4['age_rec']==3) ].groupby(['Quartile Number'])['Certainty_5'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==4) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==4) & (df4['isced']==1)]['Certainty_5'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==4) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==4) & (df4['isced']==2)]['Certainty_5'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==4) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==4) & (df4['isced']==3)]['Certainty_5'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 3')

ax3.set(ylabel='Certainty_5', xlabel='WEALTH Quartile Number for age 56 and above',  yticks = np.arange(0, 6 , 1))
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax3.errorbar(x = df4.loc[ df4['age_rec']== 4 ]['Quartile Number'].unique(), y = df4.loc[ df4['age_rec']==4  ].groupby(['Quartile Number'])['Certainty_5'].mean(), yerr = df4.loc[ (df4['age_rec']==4) ].groupby(['Quartile Number'])['Certainty_5'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==2) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==2) & (df4['isced']==1)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==2) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==2) & (df4['isced']==2)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==2) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==2) & (df4['isced']==3)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='RiskComprehension_5', xlabel='WEALTH Quartile Number for age 18 to 35',  yticks = np.arange(0, 3 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax1.errorbar(x = df4.loc[ df4['age_rec']==2  ]["Quartile Number"].unique(), y = df4.loc[ df4['age_rec']==2  ].groupby(['Quartile Number'])['RiskComprehension_5'].mean(), yerr = df4.loc[ (df4['age_rec']==2) ].groupby(['Quartile Number'])['RiskComprehension_5'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==3) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==3) & (df4['isced']==1)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==3) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==3) & (df4['isced']==2)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==3) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==3) & (df4['isced']==3)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='RiskComprehension_5', xlabel='WEALTH Quartile Number for age 36 to 55',  yticks = np.arange(0, 3 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax2.errorbar(x = df4.loc[ df4['age_rec']==3  ]["Quartile Number"].unique(), y = df4.loc[ df4['age_rec']==3  ].groupby(['Quartile Number'])['RiskComprehension_5'].mean(), yerr = df4.loc[ (df4['age_rec']==3) ].groupby(['Quartile Number'])['RiskComprehension_5'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==4) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==4) & (df4['isced']==1)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==4) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==4) & (df4['isced']==2)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==4) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==4) & (df4['isced']==3)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 3')

ax3.set(ylabel='RiskComprehension_5', xlabel='WEALTH Quartile Number for age 56 and above',  yticks = np.arange(0, 3 , 1))
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax3.errorbar(x = df4.loc[ df4['age_rec']== 4 ]['Quartile Number'].unique(), y = df4.loc[ df4['age_rec']==4  ].groupby(['Quartile Number'])['RiskComprehension_5'].mean(), yerr = df4.loc[ (df4['age_rec']==4) ].groupby(['Quartile Number'])['RiskComprehension_5'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==2) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==2) & (df4['isced']==1)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==2) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==2) & (df4['isced']==2)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==2) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==2) & (df4['isced']==3)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='Numeracy_2', xlabel='WEALTH Quartile Number for age 18 to 35',  yticks = np.arange(0, 6 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax1.errorbar(x = df4.loc[ df4['age_rec']==2  ]["Quartile Number"].unique(), y = df4.loc[ df4['age_rec']==2  ].groupby(['Quartile Number'])['Numeracy_2'].mean(), yerr = df4.loc[ (df4['age_rec']==2) ].groupby(['Quartile Number'])['Numeracy_2'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==3) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==3) & (df4['isced']==1)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==3) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==3) & (df4['isced']==2)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==3) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==3) & (df4['isced']==3)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Numeracy_2', xlabel='WEALTH Quartile Number for age 36 to 55',  yticks = np.arange(0, 6 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax2.errorbar(x = df4.loc[ df4['age_rec']==3  ]["Quartile Number"].unique(), y = df4.loc[ df4['age_rec']==3  ].groupby(['Quartile Number'])['Numeracy_2'].mean(), yerr = df4.loc[ (df4['age_rec']==3) ].groupby(['Quartile Number'])['Numeracy_2'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==4) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==4) & (df4['isced']==1)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==4) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==4) & (df4['isced']==2)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==4) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==4) & (df4['isced']==3)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 3')

ax3.set(ylabel='Numeracy_2', xlabel='WEALTH Quartile Number for age 56 and above',  yticks = np.arange(0, 6 , 1))
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax3.errorbar(x = df4.loc[ df4['age_rec']== 4 ]['Quartile Number'].unique(), y = df4.loc[ df4['age_rec']==4  ].groupby(['Quartile Number'])['Numeracy_2'].mean(), yerr = df4.loc[ (df4['age_rec']==4) ].groupby(['Quartile Number'])['Numeracy_2'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==2) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==2) & (df4['isced']==1)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==2) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==2) & (df4['isced']==2)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==2) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==2) & (df4['isced']==3)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='GraphLiteracy_3', xlabel='WEALTH Quartile Number for age 18 to 35',  yticks = np.arange(0, 4 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax1.errorbar(x = df4.loc[ df4['age_rec']==2  ]["Quartile Number"].unique(), y = df4.loc[ df4['age_rec']==2  ].groupby(['Quartile Number'])['GraphLiteracy_3'].mean(), yerr = df4.loc[ (df4['age_rec']==2) ].groupby(['Quartile Number'])['GraphLiteracy_3'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==3) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==3) & (df4['isced']==1)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==3) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==3) & (df4['isced']==2)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==3) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==3) & (df4['isced']==3)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='GraphLiteracy_3', xlabel='WEALTH Quartile Number for age 36 to 55',  yticks = np.arange(0, 4 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax2.errorbar(x = df4.loc[ df4['age_rec']==3  ]["Quartile Number"].unique(), y = df4.loc[ df4['age_rec']==3  ].groupby(['Quartile Number'])['GraphLiteracy_3'].mean(), yerr = df4.loc[ (df4['age_rec']==3) ].groupby(['Quartile Number'])['GraphLiteracy_3'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==4) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==4) & (df4['isced']==1)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==4) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==4) & (df4['isced']==2)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==4) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==4) & (df4['isced']==3)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 3')

ax3.set(ylabel='GraphLiteracy_3', xlabel='WEALTH Quartile Number for age 56 and above',  yticks = np.arange(0, 4 , 1))
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax3.errorbar(x = df4.loc[ df4['age_rec']== 4 ]['Quartile Number'].unique(), y = df4.loc[ df4['age_rec']==4  ].groupby(['Quartile Number'])['GraphLiteracy_3'].mean(), yerr = df4.loc[ (df4['age_rec']==4) ].groupby(['Quartile Number'])['GraphLiteracy_3'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==2) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==2) & (df4['isced']==1)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==2) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==2) & (df4['isced']==2)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==2) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==2) & (df4['isced']==3)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='Bayesianreasoning_1', xlabel='WEALTH Quartile Number for age 18 to 35',  yticks = np.arange(0, 5 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax1.errorbar(x = df4.loc[ df4['age_rec']==2  ]["Quartile Number"].unique(), y = df4.loc[ df4['age_rec']==2  ].groupby(['Quartile Number'])['Bayesianreasoning_1'].mean(), yerr = df4.loc[ (df4['age_rec']==2) ].groupby(['Quartile Number'])['Bayesianreasoning_1'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==3) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==3) & (df4['isced']==1)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==3) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==3) & (df4['isced']==2)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==3) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==3) & (df4['isced']==3)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Bayesianreasoning_1', xlabel='WEALTH Quartile Number for age 36 to ',  yticks = np.arange(0, 5 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax2.errorbar(x = df4.loc[ df4['age_rec']==3  ]["Quartile Number"].unique(), y = df4.loc[ df4['age_rec']==3  ].groupby(['Quartile Number'])['Bayesianreasoning_1'].mean(), yerr = df4.loc[ (df4['age_rec']==3) ].groupby(['Quartile Number'])['Bayesianreasoning_1'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==4) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==4) & (df4['isced']==1)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==4) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==4) & (df4['isced']==2)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age_rec']==4) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age_rec']==4) & (df4['isced']==3)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 3')

ax3.set(ylabel='Bayesianreasoning_1', xlabel='WEALTH Quartile Number for age 56 and above',  yticks = np.arange(0, 5 , 1))
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax3.errorbar(x = df4.loc[ df4['age_rec']== 4 ]['Quartile Number'].unique(), y = df4.loc[ df4['age_rec']==4  ].groupby(['Quartile Number'])['Bayesianreasoning_1'].mean(), yerr = df4.loc[ (df4['age_rec']==4) ].groupby(['Quartile Number'])['Bayesianreasoning_1'].sem(), fmt='o', color = lighten_color('gray',0.5))
plt.show()

# Age mean method
# Age_mean

age_m = df2["age"].median()
age_m

30.5

df4["Quartile Number"].value_counts()

Quartile Number
1    240
2    229
3    229
4    208
Name: count, dtype: int64

df4.loc[(df4['age'] < age_m)].describe()

df4.loc[(df4['age'] >= age_m)].describe()

fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df4.loc[(df4['age'] < age_m) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] < age_m) & (df4['isced']==1)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (data = df4.loc[(df4['age'] < age_m) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] < age_m) & (df4['isced']==2)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (data = df4.loc[(df4['age'] < age_m) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] < age_m) & (df4['isced']==3)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='Total Scores_19', xlabel='WEALTH Quartile Number for < median age')
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df4.loc[(df4['age'] >= age_m) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] >=age_m) & (df4['isced']==1)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (data = df4.loc[(df4['age'] >=age_m) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] >=age_m) & (df4['isced']==2)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (data = df4.loc[(df4['age'] >=age_m) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] >=age_m) & (df4['isced']==3)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Total Scores_19', xlabel='WEALTH Quartile Number for >=median age')
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age'] < age_m) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] < age_m) & (df4['isced']==1)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age'] < age_m) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] < age_m) & (df4['isced']==2)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age'] < age_m) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] < age_m) & (df4['isced']==3)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.errorbar(x = df4.loc[ (df4['age'] < age_m)  ]["Quartile Number"].unique(), y = df4.loc[ (df4['age'] < age_m)  ].groupby(['Quartile Number'])['TotalScore_18'].mean(), yerr = df4.loc[ (df4['age'] < age_m) ].groupby(['Quartile Number'])['TotalScore_18'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax1.set(ylabel='Total Scores_19', xlabel='WEALTH Quartile Number for < median age',  yticks = np.arange(5, 16 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age'] >=age_m) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] >=age_m) & (df4['isced']==1)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age'] >=age_m) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] >=age_m) & (df4['isced']==2)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age'] >=age_m) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] >=age_m) & (df4['isced']==3)]['TotalScore_18'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.errorbar(x = df4.loc[ (df4['age'] >= age_m)  ]["Quartile Number"].unique(), y = df4.loc[ (df4['age'] >=age_m)  ].groupby(['Quartile Number'])['TotalScore_18'].mean(), yerr = df4.loc[ (df4['age'] >=age_m) ].groupby(['Quartile Number'])['TotalScore_18'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax2.set(ylabel='Total Scores_19', xlabel='WEALTH Quartile Number for >=median age',  yticks = np.arange(5, 16 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age'] < age_m) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] < age_m) & (df4['isced']==1)]['Certainty_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age'] < age_m) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] < age_m) & (df4['isced']==2)]['Certainty_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age'] < age_m) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] < age_m) & (df4['isced']==3)]['Certainty_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.errorbar(x = df4.loc[ (df4['age'] < age_m)  ]["Quartile Number"].unique(), y = df4.loc[ (df4['age'] < age_m)  ].groupby(['Quartile Number'])['Certainty_5'].mean(), yerr = df4.loc[ (df4['age'] < age_m) ].groupby(['Quartile Number'])['Certainty_5'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax1.set(ylabel='Certainty_5', xlabel='WEALTH Quartile Number for < median age',  yticks = np.arange(0, 6 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age'] >=age_m) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] >=age_m) & (df4['isced']==1)]['Certainty_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age'] >=age_m) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] >=age_m) & (df4['isced']==2)]['Certainty_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age'] >=age_m) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] >=age_m) & (df4['isced']==3)]['Certainty_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.errorbar(x = df4.loc[ (df4['age'] >= age_m)  ]["Quartile Number"].unique(), y = df4.loc[ (df4['age'] >=age_m)  ].groupby(['Quartile Number'])['Certainty_5'].mean(), yerr = df4.loc[ (df4['age'] >=age_m) ].groupby(['Quartile Number'])['Certainty_5'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax2.set(ylabel='Certainty_5', xlabel='WEALTH Quartile Number for >=median age')
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age'] < age_m) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] < age_m) & (df4['isced']==1)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age'] < age_m) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] < age_m) & (df4['isced']==2)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age'] < age_m) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] < age_m) & (df4['isced']==3)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.errorbar(x = df4.loc[ (df4['age'] < age_m)  ]["Quartile Number"].unique(), y = df4.loc[ (df4['age'] < age_m)  ].groupby(['Quartile Number'])['RiskComprehension_5'].mean(), yerr = df4.loc[ (df4['age'] < age_m) ].groupby(['Quartile Number'])['RiskComprehension_5'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax1.set(ylabel='RiskComprehension_5', xlabel='WEALTH Quartile Number for < median age',  yticks = np.arange(0, 3 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age'] >=age_m) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] >=age_m) & (df4['isced']==1)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age'] >=age_m) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] >=age_m) & (df4['isced']==2)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age'] >=age_m) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] >=age_m) & (df4['isced']==3)]['RiskComprehension_5'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.errorbar(x = df4.loc[ (df4['age'] >= age_m)  ]["Quartile Number"].unique(), y = df4.loc[ (df4['age'] >=age_m)  ].groupby(['Quartile Number'])['RiskComprehension_5'].mean(), yerr = df4.loc[ (df4['age'] >=age_m) ].groupby(['Quartile Number'])['RiskComprehension_5'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax2.set(ylabel='RiskComprehension_5', xlabel='WEALTH Quartile Number for >=median age',  yticks = np.arange(0, 3 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age'] < age_m) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] < age_m) & (df4['isced']==1)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age'] < age_m) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] < age_m) & (df4['isced']==2)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age'] < age_m) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] < age_m) & (df4['isced']==3)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.errorbar(x = df4.loc[ (df4['age'] < age_m)  ]["Quartile Number"].unique(), y = df4.loc[ (df4['age'] < age_m)  ].groupby(['Quartile Number'])['Numeracy_2'].mean(), yerr = df4.loc[ (df4['age'] < age_m) ].groupby(['Quartile Number'])['Numeracy_2'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax1.set(ylabel='Numeracy_2', xlabel='WEALTH Quartile Number for < median age',  yticks = np.arange(0, 6 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age'] >=age_m) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] >=age_m) & (df4['isced']==1)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age'] >=age_m) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] >=age_m) & (df4['isced']==2)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age'] >=age_m) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] >=age_m) & (df4['isced']==3)]['Numeracy_2'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.errorbar(x = df4.loc[ (df4['age'] >= age_m)  ]["Quartile Number"].unique(), y = df4.loc[ (df4['age'] >=age_m)  ].groupby(['Quartile Number'])['Numeracy_2'].mean(), yerr = df4.loc[ (df4['age'] >=age_m) ].groupby(['Quartile Number'])['Numeracy_2'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax2.set(ylabel='Numeracy_2', xlabel='WEALTH Quartile Number for >=median age',  yticks = np.arange(0, 6 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age'] < age_m) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] < age_m) & (df4['isced']==1)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age'] < age_m) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] < age_m) & (df4['isced']==2)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age'] < age_m) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] < age_m) & (df4['isced']==3)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.errorbar(x = df4.loc[ (df4['age'] < age_m)  ]["Quartile Number"].unique(), y = df4.loc[ (df4['age'] < age_m)  ].groupby(['Quartile Number'])['GraphLiteracy_3'].mean(), yerr = df4.loc[ (df4['age'] < age_m) ].groupby(['Quartile Number'])['GraphLiteracy_3'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax1.set(ylabel='GraphLiteracy_3', xlabel='WEALTH Quartile Number for < median age',  yticks = np.arange(0, 4 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age'] >=age_m) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] >=age_m) & (df4['isced']==1)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age'] >=age_m) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] >=age_m) & (df4['isced']==2)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age'] >=age_m) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] >=age_m) & (df4['isced']==3)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.errorbar(x = df4.loc[ (df4['age'] >= age_m)  ]["Quartile Number"].unique(), y = df4.loc[ (df4['age'] >=age_m)  ].groupby(['Quartile Number'])['GraphLiteracy_3'].mean(), yerr = df4.loc[ (df4['age'] >=age_m) ].groupby(['Quartile Number'])['GraphLiteracy_3'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax2.set(ylabel='GraphLiteracy_3', xlabel='WEALTH Quartile Number for >=median age',  yticks = np.arange(0, 4 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age'] < age_m) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] < age_m) & (df4['isced']==1)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age'] < age_m) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] < age_m) & (df4['isced']==2)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age'] < age_m) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] < age_m) & (df4['isced']==3)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.errorbar(x = df4.loc[ (df4['age'] < age_m)  ]["Quartile Number"].unique(), y = df4.loc[ (df4['age'] < age_m)  ].groupby(['Quartile Number'])['Bayesianreasoning_1'].mean(), yerr = df4.loc[ (df4['age'] < age_m) ].groupby(['Quartile Number'])['Bayesianreasoning_1'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax1.set(ylabel='Bayesianreasoning_1', xlabel='WEALTH Quartile Number for < median age',  yticks = np.arange(0, 5 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df4.loc[(df4['age'] >=age_m) & (df4['isced']==1)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] >=age_m) & (df4['isced']==1)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df4.loc[(df4['age'] >=age_m) & (df4['isced']==2)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] >=age_m) & (df4['isced']==2)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df4.loc[(df4['age'] >=age_m) & (df4['isced']==3)].reset_index(drop = True), x = 'Quartile Number', y = df4.loc[(df4['age'] >=age_m) & (df4['isced']==3)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.errorbar(x = df4.loc[ (df4['age'] >= age_m)  ]["Quartile Number"].unique(), y = df4.loc[ (df4['age'] >=age_m)  ].groupby(['Quartile Number'])['Bayesianreasoning_1'].mean(), yerr = df4.loc[ (df4['age'] >=age_m) ].groupby(['Quartile Number'])['Bayesianreasoning_1'].sem(), fmt='o', color = lighten_color('gray',0.5))
ax2.set(ylabel='Bayesianreasoning_1', xlabel='WEALTH Quartile Number for >=median age',  yticks = np.arange(0, 5 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

	ResponseId	Please indicate your Gender.	Please mark your age (in years)	What is currently your highest Education?	Please state your current occupation.	How do you describe your willingness to take financial risk in general?	Given the number of years that you have held various investments and the amount of investing you might have done, what degree of investment experience in the stock market do you have?	If an expert tries to worry or scare me, i.e. a financial advisor about my financial situation, I choose another expert.	I only buy a financial product I understand.	I trust doctors.	When I want to buy a bigger item like a refrigerator or an expensive item of clothing, I wait a month to see whether I still want it and only buy it then.	I always keep in mind that everything I do on the web could be used to my disadvantage.	In my household, we/I spend:	In my household, we/I	_Distribution 1_\n\nHow risky do you perceive the investment to be?	_Distribution 2_\n\nHow risky do you perceive the investment to be?	_Distribution 3_\n\nHow risky do you perceive the investment to be?	_Distribution 4_\n\nHow risky do you perceive the investment to be?	_Distribution 5_\n\nHow risky do you perceive the investment to be?	Distribution 6\n\nHow risky do you perceive the investment to be?	Distribution 7\n\nHow risky do you perceive the investment to be?	Distribution 8\n\nHow risky do you perceive the investment to be?	Mumbai A = 9 out of 10000 OR B = 1 out of 1000	Bengaluru A = 0.7% OR B = 0.099%	Kolkata A = 0.61% OR B = 6 out of 10000	HIV test	Fingerprint	DNA test	Cancer screening test	Professional horoscope	A study estimates that eating 100g chocolate everyday increases the risk of obesity by 20%. Which of the following statements is true?	There is an official prediction that the national stock market will grow 2% annually over the next 5 years. This means that…	Imagine you are told that the price of the stock Soya Ruchi increases from INR 60 to INR 120 after the company merger. What does this mean?	It is predicted that Indigo Bank has 30% chance of default next year. Which of the following alternatives is the most appropriate interpretation of the statement?	The probability that the economy will go into a recession this year is 30%. If the economy goes into recession, the probability that the stock market will decrease is 80%. If the economy does not go into a recession, the probability that the stock market will decrease is 23%. What is the probability that the economy goes into recession given that the stock market decreased?	A new policy intervention increases the number of people who are employed by 20%. This statistic implies that the intervention increases the number of people who are employed from:	Imagine you are told that a new medication increases the number of people who recover from a disease from 2 out of 1,000 to 4 out of 1,000. This implies:	Imagine that we flip a fair coin 1,000 times. What is your best guess about how many times the coin will come up heads in 1,000 flips? \n\n\_\_\_\_\_\_ times out of 1,000.	In the Bingo Lottery, the chance of winning a $10 prize is 1%. What is your best guess about how many people will win a $10 prize if 1,000 people each buy a single ticket for Bingo Lottery?\n\n\_\_\_\_\_\_ person(s) out of 1,000.	In a sweepstakes, the chance of winning a car is 1 in 1,000. What percentage of tickets for the sweepstakes wins a car?\n\n\_\_\_\_\_ % of tickets	About 10 out of 1,000 children develop Down syndrome. In a Down syndrome test, 9 out of these 10 children with Down syndrome tested positive. Out of the 990 children without Down syndrome 50 nevertheless tested positive. Among those women with a positive test result concerning their child how many actually have a child with Down syndrome? [Select one response only]	Approximately what percentage (%) of people who die from cancer die from colon cancer, breast cancer, and prostate cancer taken together?	The following figure shows the number of men and women among a group of smartphone users. The total number of circles is 100. \n\nHow many more men than women are there among the 100 people using a smartphone?	In a magazine you see two advertisements, one on page 5 and another on page 12. Each is for a different drug for treating heart disease, and each includes a graph showing the effectiveness of the drug compared to a placebo (sugar pill).\n\nCompared to the placebo, which treatment leads to a larger decrease in the percentage of patients who die?	Please indicate your approximate annual personal income from all sources for last year	Please provide a rough guess (in Indian Rupees) of the worth of your household's assets. Please do not forget to correct it for your debts, such as a mortgage or any loans you might have.	uid	Response Type	Start Date (UTC)	Stage Date (UTC)	Submit Date (UTC)	Network ID	LTA
0	wjkzxyv1zx3q7oxgmtwwjwkq24ey2n48	Male	25 - 35	Post Graduate	Investment Professional, i.e. stock broker/tra...	5	8	Somewhat	Completely	Completely	Completely	Completely	more than half of the household income and sav...	write down budget and spending, and have an em...	6	6	7	7	4	3	7	7	A	B	A	Yes	Yes	Yes	No	Yes	Irrespective of the quality of the study, futu...	the growth rate over five years will be exactl...	the stock price increased by 50%	30% of central bankers think that Indigo Bank ...	60%	70 in 100 people prior to the intervention to ...	The medication increases recovery by 100%	50	100	1	59 out of 100	25	20	They are equal	5,00,000 - 15,00,000	100000	MX0O31725956661qRo	completed	2024-09-10 08:26:07	NaN	2024-09-10 08:34:54	89640d1b73	6.099537
1	fbv4e1fblmp0yreyfbv4eifpzx4ax7zs	Male	36 - 45	Ph.D. or higher	Investment Professional, i.e. stock broker/tra...	6	8	Just about	Completely	Completely	Just about	Completely	less than half of the household income and sav...	have an emergency fund, and spend as it feels ...	5	6	7	6	5	7	6	5	B	A	A	No	Yes	Yes	No	No	The lower the quality of the study, the more l...	the growth rate over five years will be exactl...	the stock price increased by 100%	30% of central bankers think that Indigo Bank ...	30%	70 in 100 people prior to the intervention to ...	None of the above is implied.	50	10	1	59 out of 1000	40	20	Crosicol	15,00,001 - 25,00,000	7200000	MXGDh1725956571Vdc	completed	2024-09-10 08:26:04	NaN	2024-09-10 08:36:25	e27c0ccdab	7.187500
2	6458w9qj8sqw7ldtt56457hzeduqz2qb	Female	36 - 45	Post Graduate	Employee/Consultant in Other than the Finance ...	6	10	Completely	Just about	Completely	Just about	Completely	all or more than the household income, even th...	have an emergency fund, and spend as it feels ...	7	3	6	7	6	7	6	7	B	A	B	Yes	Yes	Yes	Yes	Yes	The lower the quality of the study, the more l...	the growth rate over five years will be betwee...	the stock price increased by 100%	30% of central bankers think that Indigo Bank ...	80%	100 in 10,000 people prior to the intervention...	The medication increases recovery by 100%	10	100	10	59 out of 100	20	10	Crosicol	35,00,001 - 45,00,000	10	MXzTR1725956643Vmr	completed	2024-09-10 08:25:58	NaN	2024-09-10 08:33:57	942e517f31	5.543981
3	9woo3kquplxhob9whhbfvhleezgy3m8l	Male	25 - 35	Post Graduate	Employee/Consultant in Other than the Finance ...	5	3	Somewhat	Somewhat	Somewhat	Not at all	Somewhat	all or more than our household income, because...	write down budget and spending, and have an em...	4	2	4	1	1	7	6	3	A	B	A	Yes	Yes	Yes	No	No	Irrespective of the quality of the study, futu...	the growth rate over five years will be betwee...	the stock price increased by 50%	Banks similar to Indigo will default 30% of th...	30%	5 in 100 people prior to the intervention to 6...	The medication increases recovery by 2%	500	10	10	59 out of 1000	50	10	Hertinol	5,00,000 - 15,00,000	50000	MXhzL1725956594UqF	completed	2024-09-10 08:25:48	NaN	2024-09-10 08:35:16	dcc2ff7969	6.574074
5	huy0uxnebqrbqdegrz2sxhofkhuy0ux0	Female	25 - 35	Under Graduate	Home Maker or not employed	5	0	Somewhat	Moderately	Moderately	Somewhat	Not at all	all or more than the household income, even th...	have an emergency fund, and spend as it feels ...	5	4	3	6	1	2	7	3	A	A	B	Yes	Yes	Yes	Yes	Yes	The higher the quality of the study, the more ...	the growth rate over five years will be exactl...	the stock price increased by 100%	30% of the banks customers will default next year	30%	5 in 100 people prior to the intervention to 6...	None of the above is implied.	20	1	1	59 out of 100	50	40	Can’t say	< 5,00,000	500000	MXWUf1725956389QtP	completed	2024-09-10 08:25:04	NaN	2024-09-10 08:34:00	b9b7c6efb9	6.203704
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
638	tod55nr27ozkyci5tod5rup87g8lo9ss	Male	18 - 25	Diploma or vocation training	Entrepreneur or Own Business	4	2	Somewhat	Moderately	Moderately	Somewhat	Moderately	all or more than our household income, because...	write down budget and spending, and have an em...	1	4	4	2	5	5	0	4	B	B	A	Yes	Yes	Yes	No	No	Irrespective of the quality of the study, futu...	the growth rate will be 0.4% on average each year	an answer is not possible based on the informa...	The bank will default on 30% of repayments in ...	50%	70 in 100 people prior to the intervention to ...	None of the above is implied.	500	10	1	59 out of 100	50	10	Hertinol	25,00,001 - 35,00,000	5000000	MXLz61724688143Vic	completed	2024-08-26 16:04:07	NaN	2024-08-26 16:12:43	e7e6df96c7	5.972222
639	phtgoyb2qh5lc4mhzq1fcphtgo8o361s	Male	25 - 35	Diploma or vocation training	Investment Professional, i.e. stock broker/tra...	7	10	Just about	Just about	Just about	Just about	Just about	less than half of the household income and sav...	write down budget and spending, but have no em...	7	7	7	7	7	6	2	3	B	B	B	Yes	Yes	Yes	Yes	Yes	Irrespective of the quality of the study, futu...	the growth rate will be 0.4% on average each year	the stock price increased by 60%	The bank will default on 30% of repayments in ...	50%	it is not possible to determine which of the a...	The medication increases recovery by 2%	1000	1000	10	59 out of 1000	50	100	They are equal	15,00,001 - 25,00,000	1000000	MXtZg1724688171JNW	completed	2024-08-26 16:04:00	NaN	2024-08-26 16:12:06	cfe4da8641	5.625000
640	c2ba33tn344exordvkc2baixvpwi0b7s	Female	25 - 35	Diploma or vocation training	Investment Professional, i.e. stock broker/tra...	1	2	Just about	Not at all	Just about	Just about	Not at all	all or more than the household income, even th...	write down budget and spending, but have no em...	0	0	1	1	0	0	2	0	A	A	B	No	Yes	Yes	No	Yes	The lower the quality of the study, the more l...	it is not possible to predict the growth rate ...	the stock price increased by 50%	The bank will default on 30% of repayments in ...	60%	it is not possible to determine which of the a...	The medication increases recovery by 100%	1000	500	10	59 out of 1000	10	60	They are equal	5,00,000 - 15,00,000	100000	MXlQt1724679500yOJ	completed	2024-08-26 13:41:51	NaN	2024-08-26 13:59:39	9eaccc3462	12.361111
641	13nxdmew77mtq97scc6r13nxduhpev0l	Male	25 - 35	Post Graduate	Entrepreneur or Own Business	5	9	Moderately	Moderately	Moderately	Moderately	Somewhat	less than half of the household income and sav...	have an emergency fund, and spend as it feels ...	3	3	5	1	0	3	7	2	A	B	A	Yes	Yes	Yes	Yes	No	The higher the quality of the study, the more ...	the growth rate will be 0.4% on average each year	the stock price increased by 100%	30% of the banks customers will default next year	30%	100 in 10,000 people prior to the intervention...	The medication increases recovery by 50%	500	100	1	59 out of 1000	25	60	Crosicol	15,00,001 - 25,00,000	13000000	MXcTq1724679467kFu	completed	2024-08-26 13:39:40	NaN	2024-08-26 13:49:13	5a7cce9582	6.631944
642	s5m2omt6lyzdyeb1k9cs5m2omtmv05c3	Female	18 - 25	Post Graduate	Employee/Consultant in Other than the Finance ...	6	1	Somewhat	Moderately	Moderately	Moderately	Moderately	more than half of the household income and sav...	write down budget and spending, and have an em...	5	0	6	1	6	6	0	6	A	A	A	Yes	Yes	Yes	Yes	No	The lower the quality of the study, the more l...	it is not possible to predict the growth rate ...	the stock price increased by 50%	The bank will default on 30% of repayments in ...	30%	70 in 100 people prior to the intervention to ...	The medication increases recovery by 50%	500	10	1	59 out of 1000	25	60	Crosicol	15,00,001 - 25,00,000	100000	MX4Er1724679488ZwW	completed	2024-08-26 13:39:25	NaN	2024-08-26 13:54:38	3e73e75dfb	10.567130

		ResponseId	Please indicate your Gender.	Please mark your age (in years)	What is currently your highest Education?	Please state your current occupation.	How do you describe your willingness to take financial risk in general?	Given the number of years that you have held various investments and the amount of investing you might have done, what degree of investment experience in the stock market do you have?	If an expert tries to worry or scare me, i.e. a financial advisor about my financial situation, I choose another expert.	I only buy a financial product I understand.	I trust doctors.	When I want to buy a bigger item like a refrigerator or an expensive item of clothing, I wait a month to see whether I still want it and only buy it then.	I always keep in mind that everything I do on the web could be used to my disadvantage.	In my household, we/I spend:	In my household, we/I	_Distribution 1_\n\nHow risky do you perceive the investment to be?	_Distribution 2_\n\nHow risky do you perceive the investment to be?	_Distribution 3_\n\nHow risky do you perceive the investment to be?	_Distribution 4_\n\nHow risky do you perceive the investment to be?	_Distribution 5_\n\nHow risky do you perceive the investment to be?	Distribution 6\n\nHow risky do you perceive the investment to be?	Distribution 7\n\nHow risky do you perceive the investment to be?	Distribution 8\n\nHow risky do you perceive the investment to be?	Mumbai A = 9 out of 10000 OR B = 1 out of 1000	Bengaluru A = 0.7% OR B = 0.099%	Kolkata A = 0.61% OR B = 6 out of 10000	HIV test	Fingerprint	DNA test	Cancer screening test	Professional horoscope	A study estimates that eating 100g chocolate everyday increases the risk of obesity by 20%. Which of the following statements is true?	There is an official prediction that the national stock market will grow 2% annually over the next 5 years. This means that…	Imagine you are told that the price of the stock Soya Ruchi increases from INR 60 to INR 120 after the company merger. What does this mean?	It is predicted that Indigo Bank has 30% chance of default next year. Which of the following alternatives is the most appropriate interpretation of the statement?	The probability that the economy will go into a recession this year is 30%. If the economy goes into recession, the probability that the stock market will decrease is 80%. If the economy does not go into a recession, the probability that the stock market will decrease is 23%. What is the probability that the economy goes into recession given that the stock market decreased?	A new policy intervention increases the number of people who are employed by 20%. This statistic implies that the intervention increases the number of people who are employed from:	Imagine you are told that a new medication increases the number of people who recover from a disease from 2 out of 1,000 to 4 out of 1,000. This implies:	Imagine that we flip a fair coin 1,000 times. What is your best guess about how many times the coin will come up heads in 1,000 flips? \n\n\_\_\_\_\_\_ times out of 1,000.	In the Bingo Lottery, the chance of winning a $10 prize is 1%. What is your best guess about how many people will win a $10 prize if 1,000 people each buy a single ticket for Bingo Lottery?\n\n\_\_\_\_\_\_ person(s) out of 1,000.	In a sweepstakes, the chance of winning a car is 1 in 1,000. What percentage of tickets for the sweepstakes wins a car?\n\n\_\_\_\_\_ % of tickets	About 10 out of 1,000 children develop Down syndrome. In a Down syndrome test, 9 out of these 10 children with Down syndrome tested positive. Out of the 990 children without Down syndrome 50 nevertheless tested positive. Among those women with a positive test result concerning their child how many actually have a child with Down syndrome? [Select one response only]	Approximately what percentage (%) of people who die from cancer die from colon cancer, breast cancer, and prostate cancer taken together?	The following figure shows the number of men and women among a group of smartphone users. The total number of circles is 100. \n\nHow many more men than women are there among the 100 people using a smartphone?	In a magazine you see two advertisements, one on page 5 and another on page 12. Each is for a different drug for treating heart disease, and each includes a graph showing the effectiveness of the drug compared to a placebo (sugar pill).\n\nCompared to the placebo, which treatment leads to a larger decrease in the percentage of patients who die?	Please indicate your approximate annual personal income from all sources for last year	Please provide a rough guess (in Indian Rupees) of the worth of your household's assets. Please do not forget to correct it for your debts, such as a mortgage or any loans you might have.	uid	Response Type	Start Date (UTC)	Stage Date (UTC)	Submit Date (UTC)	Network ID	LTA	age_rec	age	isced	income	wealth	q8_2_1	q8_2_2	q8_2_3	q8_2_4	q8_2_5	q8_3	q8_4	q8_5berlin_1	q8_5london_1	q8_5paris_1	q8_6	q8_7	q9_1_1	q9_2_1	q9_3	q10_1_1	q10_2_1	q10_3_1	q10_4	certainty1	certainty2	certainty3	certainty4	certainty5	uncertainty1	uncertainty2	numeracy1	numeracy2	numeracy3	numeracy4	numeracy5	graph1	graph2	graph3	riskcomprehension1	riskcomprehension2	riskcomprehension3	riskcomprehension4	riskcomprehension5	bayesian1
ResponseId
00dn8csjzlekx6gzhrcv00dn8csj32tz	547	00dn8csjzlekx6gzhrcv00dn8csj32tz	Male	25 - 35	Under Graduate	Investment Professional, i.e. stock broker/tra...	7	10	Completely	Just about	Completely	Completely	Just about	all or more than the household income, even th...	have an emergency fund, and spend as it feels ...	7	7	7	6	6	6	6	7	A	B	A	Yes	Yes	Yes	Yes	Yes	The lower the quality of the study, the more l...	the growth rate will be 0.4% on average each year	the stock price increased by 50%	30% of the banks customers will default next year	30%	it is not possible to determine which of the a...	The medication increases recovery by 50%	30	20	30	9 out of 10	40	60	They are equal	5,00,000 - 15,00,000	50000	MX9oX1724844773oYI	completed	2024-08-28 11:35:35	NaN	2024-08-28 12:02:05	f30fdd629e	18.402778	2	30.5	2	2	50000	1	1	1	1	1	1	1	1	2	1	2	4	40	60	3	30	20	30	2	0	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	1	0	1	0
00ujdxbfoya0donu8r00ujcjdkojc99x	428	00ujdxbfoya0donu8r00ujcjdkojc99x	Female	25 - 35	Under Graduate	Employee/Consultant in Other than the Finance ...	7	8	Completely	Completely	Completely	Moderately	Moderately	less than half of the household income and sav...	write down budget and spending, and have an em...	2	1	1	1	0	2	0	0	B	B	A	Yes	Yes	Yes	Yes	No	The higher the quality of the study, the more ...	the growth rate over five years will be betwee...	the stock price increased by 50%	The bank will default on 30% of repayments in ...	50%	100 in 10,000 people prior to the intervention...	The medication increases recovery by 50%	500	100	1	59 out of 1000	50	10	They are equal	< 5,00,000	1500000	MXic21725178744djB	completed	2024-09-01 08:23:04	NaN	2024-09-01 09:24:44	1c1d51349c	42.824074	2	30.5	2	1	1500000	1	1	1	1	2	2	3	2	2	1	2	2	50	10	3	500	100	1	3	0	0	0	0	1	0	0	1	0	0	0	0	0	0	1	1	0	1	0	0	0
022xoawhrghfhv4a1g022xmz7hb0m41f	442	022xoawhrghfhv4a1g022xmz7hb0m41f	Female	46 - 55	Under Graduate	Entrepreneur or Own Business	5	4	Somewhat	Somewhat	Moderately	Just about	Somewhat	all or more than the household income, even th...	spend as it feel right, and do not have an eme...	4	4	4	4	4	4	4	4	B	A	B	Yes	Yes	Yes	No	No	The lower the quality of the study, the more l...	the growth rate will be 0.4% on average each year	the stock price increased by 60%	30% of the banks customers will default next year	80%	5 in 100 people prior to the intervention to 6...	The medication increases recovery by 2%	500	1	1	59 out of 1000	17	65	They are equal	< 5,00,000	15000000	MXFDH1725178659xRn	completed	2024-09-01 08:21:14	NaN	2024-09-01 08:33:49	fbd8962401	8.738426	3	50.5	2	1	15000000	1	1	1	2	2	1	1	2	1	2	3	1	17	65	3	500	1	1	3	0	0	0	1	1	1	0	1	0	0	0	0	0	0	1	1	1	0	0	0	0
02pxtdbyibecqqfvwlw02pxwfbane9zd	558	02pxtdbyibecqqfvwlw02pxwfbane9zd	Female	46 - 55	Post Graduate	Employee/Consultant in Other than the Finance ...	6	10	Not at all	Completely	Completely	Completely	Moderately	all or more than the household income, even th...	have an emergency fund, and spend as it feels ...	5	5	6	5	7	5	0	5	B	A	A	Yes	Yes	Yes	No	Yes	The higher the quality of the study, the more ...	it is not possible to predict the growth rate ...	an answer is not possible based on the informa...	30% of the banks customers will default next year	30%	5 in 100 people prior to the intervention to 6...	None of the above is implied.	1000	1	100	9 out of 10	20	20	Can’t say	5,00,000 - 15,00,000	3000000	MXIpM1724844742xet	completed	2024-08-28 11:35:05	NaN	2024-08-28 11:48:47	e8b668d11b	9.513889	3	50.5	1	2	3000000	1	1	1	2	1	2	4	2	1	1	4	1	20	20	4	1000	1	100	2	0	0	0	1	0	0	1	0	0	0	0	0	0	1	0	1	1	1	0	0	0
037aefjdt26mnjd102nz0pk037aedfhc	544	037aefjdt26mnjd102nz0pk037aedfhc	Male	46 - 55	Post Graduate	Employee/Consultant in Other than the Finance ...	3	6	Somewhat	Somewhat	Somewhat	Somewhat	Somewhat	more than half of the household income and sav...	write down budget and spending, but have no em...	3	1	6	1	2	6	7	5	A	B	A	No	No	Yes	No	No	Irrespective of the quality of the study, futu...	it is not possible to predict the growth rate ...	an answer is not possible based on the informa...	Banks similar to Indigo will default 30% of th...	60%	70 in 100 people prior to the intervention to ...	None of the above is implied.	1000	1	10	9 out of 59	50	20	Hertinol	5,00,000 - 15,00,000	7000000	MXpv61724844846UVs	completed	2024-08-28 11:35:50	NaN	2024-08-28 11:45:54	f51b723011	6.990741	3	50.5	1	2	7000000	2	2	1	2	2	3	4	1	2	1	4	3	50	20	2	1000	1	10	1	1	1	0	1	1	0	1	0	0	0	0	0	0	1	0	0	0	1	0	0	1
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
zeoufbqlngfunzzeoymhtj2sp5cw6lur	396	zeoufbqlngfunzzeoymhtj2sp5cw6lur	Male	25 - 35	Post Graduate	Investment Professional, i.e. stock broker/tra...	3	8	Not at all	Somewhat	Somewhat	Moderately	Somewhat	less than half of the household income and sav...	spend as it feel right, and do not have an eme...	3	4	4	3	6	6	3	4	B	A	A	No	Yes	No	No	No	The higher the quality of the study, the more ...	the growth rate over five years will be exactl...	the stock price increased by 100%	Banks similar to Indigo will default 30% of th...	50%	70 in 100 people prior to the intervention to ...	The medication increases recovery by 100%	500	100	1	59 out of 1000	50	40	Crosicol	< 5,00,000	50000	MXFoD1725258821KAh	completed	2024-09-02 06:36:01	NaN	2024-09-02 06:46:38	c31fa70c8d	7.372685	2	30.5	1	1	50000	2	1	2	2	2	2	2	2	1	1	1	3	50	40	1	500	100	1	3	1	0	1	1	1	0	0	1	0	0	0	0	0	0	0	1	1	1	1	0	0
zfemo30rg0ekva18x1kjwzfemo2wpg2p	185	zfemo30rg0ekva18x1kjwzfemo2wpg2p	Male	36 - 45	Ph.D. or higher	Employee/Consultant in Other than the Finance ...	6	8	Not at all	Moderately	Just about	Not at all	Not at all	less than half of the household income and sav...	have an emergency fund, and spend as it feels ...	3	1	4	2	0	5	0	2	B	A	A	No	Yes	Yes	No	No	Irrespective of the quality of the study, futu...	the growth rate over five years will be betwee...	the stock price increased by 100%	30% of central bankers think that Indigo Bank ...	30%	100 in 10,000 people prior to the intervention...	The medication increases recovery by 100%	500	10	1	59 out of 1000	25	20	Can’t say	15,00,001 - 25,00,000	8500000	MXUxt1725260651heG	completed	2024-09-02 07:08:18	NaN	2024-09-02 07:23:09	fbb956cffc	10.312500	3	40.5	1	3	8500000	2	1	1	2	2	4	3	2	1	1	1	2	25	20	4	500	10	1	3	1	0	0	1	1	0	0	1	1	0	0	0	1	1	0	1	1	1	1	0	0
znqvw4t38br3072znqvwvjsgb6wvj7nt	305	znqvw4t38br3072znqvwvjsgb6wvj7nt	Female	36 - 45	Post Graduate	Entrepreneur or Own Business	5	5	Somewhat	Completely	Just about	Moderately	Somewhat	less than half of the household income and sav...	spend as it feel right, and do not have an eme...	0	0	0	1	0	0	0	0	B	B	B	Yes	Yes	Yes	No	No	Irrespective of the quality of the study, futu...	it is not possible to predict the growth rate ...	the stock price increased by 50%	30% of the banks customers will default next year	80%	it is not possible to determine which of the a...	The medication increases recovery by 2%	500	10	1	9 out of 59	25	20	Hertinol	5,00,000 - 15,00,000	2000000	MXd921725259652XDJ	completed	2024-09-02 06:49:56	NaN	2024-09-02 07:17:20	1964f49e1c	19.027778	3	40.5	1	2	2000000	1	1	1	2	2	3	4	2	2	2	3	4	25	20	2	500	10	1	1	0	0	0	1	1	0	1	1	1	0	0	0	1	1	0	1	0	0	0	1	1
zstpq23h1x3ilab8s8vsrbkw8t4zstpq	306	zstpq23h1x3ilab8s8vsrbkw8t4zstpq	Male	18 - 25	Under Graduate	Entrepreneur or Own Business	6	9	Completely	Completely	Moderately	Completely	Moderately	all or more than our household income, because...	have an emergency fund, and spend as it feels ...	6	3	7	1	2	7	7	3	A	A	B	No	Yes	Yes	No	No	The higher the quality of the study, the more ...	the growth rate over five years will be betwee...	the stock price increased by 60%	Banks similar to Indigo will default 30% of th...	80%	5 in 100 people prior to the intervention to 6...	None of the above is implied.	750	10	1	59 out of 1000	50	10	They are equal	5,00,000 - 15,00,000	60000000	MXfmg1725259705Fkj	completed	2024-09-02 06:49:52	NaN	2024-09-02 07:01:04	03aa77ec48	7.777778	2	21.5	2	2	60000000	2	1	1	2	2	2	3	1	1	2	4	1	50	10	3	750	10	1	3	1	0	0	1	1	0	0	0	1	0	0	0	0	0	1	0	1	0	0	0	0
ztfdlcbiyo1xco3ztfdl4ozqpte2q3m9	95	ztfdlcbiyo1xco3ztfdl4ozqpte2q3m9	Male	25 - 35	Post Graduate	Entrepreneur or Own Business	6	7	Somewhat	Just about	Completely	Moderately	Somewhat	more than half of the household income and sav...	spend as it feel right, and do not have an eme...	6	0	2	5	3	7	0	4	B	A	A	No	Yes	Yes	Yes	Yes	The higher the quality of the study, the more ...	it is not possible to predict the growth rate ...	the stock price increased by 100%	30% of central bankers think that Indigo Bank ...	80%	5 in 100 people prior to the intervention to 6...	The medication increases recovery by 2%	500	10	1	59 out of 1000	25	60	Can’t say	15,00,001 - 25,00,000	5000000	MXjp51725955476hMt	completed	2024-09-10 08:06:34	NaN	2024-09-10 08:16:35	f68294192e	6.956019	2	30.5	1	3	5000000	2	1	1	1	1	2	4	2	1	1	3	1	25	60	4	500	10	1	3	1	0	0	0	0	0	1	1	1	0	0	0	1	0	0	1	1	1	0	0	0

		ResponseId	age	age_rec	isced	income	wealth	Certainty_5	Uncertainty_2	RiskComprehension_5	GraphLiteracy_3	Numeracy_2	Bayesianreasoning_1	Certainty_%	Uncertainty_%	RiskComprehension_%	GraphLiteracy_%	Numeracy_%	Bayesianreasoning_%	TotalScore_18	TotalScore_%
ResponseId
00dn8csjzlekx6gzhrcv00dn8csj32tz	547	00dn8csjzlekx6gzhrcv00dn8csj32tz	30.5	2	2	2	50000	0	1	2	1	0	0	0.0	50.0	40.0	33.333333	0.0	0.0	4	22.222222
00ujdxbfoya0donu8r00ujcjdkojc99x	428	00ujdxbfoya0donu8r00ujcjdkojc99x	30.5	2	2	1	1500000	1	0	2	1	1	0	20.0	0.0	40.0	33.333333	50.0	0.0	5	27.777778
022xoawhrghfhv4a1g022xmz7hb0m41f	442	022xoawhrghfhv4a1g022xmz7hb0m41f	50.5	3	2	1	15000000	2	1	2	1	1	0	40.0	50.0	40.0	33.333333	50.0	0.0	7	38.888889
02pxtdbyibecqqfvwlw02pxwfbane9zd	558	02pxtdbyibecqqfvwlw02pxwfbane9zd	50.5	3	1	2	3000000	1	1	3	1	0	0	20.0	50.0	60.0	33.333333	0.0	0.0	6	33.333333
037aefjdt26mnjd102nz0pk037aedfhc	544	037aefjdt26mnjd102nz0pk037aedfhc	50.5	3	1	2	7000000	4	1	1	1	0	1	80.0	50.0	20.0	33.333333	0.0	100.0	8	44.444444
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
zeoufbqlngfunzzeoymhtj2sp5cw6lur	396	zeoufbqlngfunzzeoymhtj2sp5cw6lur	30.5	2	1	1	50000	4	0	4	0	1	0	80.0	0.0	80.0	0.000000	50.0	0.0	9	50.000000
zfemo30rg0ekva18x1kjwzfemo2wpg2p	185	zfemo30rg0ekva18x1kjwzfemo2wpg2p	40.5	3	1	3	8500000	3	0	4	2	2	0	60.0	0.0	80.0	66.666667	100.0	0.0	11	61.111111
znqvw4t38br3072znqvwvjsgb6wvj7nt	305	znqvw4t38br3072znqvwvjsgb6wvj7nt	40.5	3	1	2	2000000	2	1	2	2	2	1	40.0	50.0	40.0	66.666667	100.0	100.0	10	55.555556
zstpq23h1x3ilab8s8vsrbkw8t4zstpq	306	zstpq23h1x3ilab8s8vsrbkw8t4zstpq	21.5	2	2	2	60000000	3	0	1	1	1	0	60.0	0.0	20.0	33.333333	50.0	0.0	6	33.333333
ztfdlcbiyo1xco3ztfdl4ozqpte2q3m9	95	ztfdlcbiyo1xco3ztfdl4ozqpte2q3m9	30.5	2	1	3	5000000	1	1	3	1	2	0	20.0	50.0	60.0	33.333333	100.0	0.0	8	44.444444

		ResponseId	Q0	Q1	Q2	Q3	Q4	Q5_1	Q6_1	Q8_1	Q8_2	Q8_3	Q8_4	Q8_5	Q8_6	Q8_7	Q8_8	Q9b_1	Q9b_4	Q9b_5	Q9b_6	Q9b_7	Q9b_8	Q9b_9	Q11a_1	Q11a_2	Q11a_3	Q11b	Q11c	Q11d	Q11h	Attention Check	Q11i	Q12a	Q12b	Q12c	Q13a	Q13b	Q13c	Q13d	Q14a	Q14b	Q14c	Q15a	Q14b.1	age	age_rec	isced	income	wealth	q8_2_1	q8_2_2	q8_2_3	q8_2_4	q8_2_5	q8_3	q8_4	q8_5berlin_1	q8_5london_1	q8_5paris_1	q8_6	q8_7	q9_1_1	q9_2_1	q9_3	q10_1_1	q10_2_1	q10_3_1	q10_4	certainty1	certainty2	certainty3	certainty4	certainty5	uncertainty1	uncertainty2	numeracy1	numeracy2	numeracy3	numeracy4	numeracy5	graph1	graph2	graph3	riskcomprehension1	riskcomprehension2	riskcomprehension3	riskcomprehension4	riskcomprehension5	bayesian1
ResponseId
R_1YkMM2lMB9aEuVL	150	R_1YkMM2lMB9aEuVL	Yes, I would like to participate in the study ...	Female	41	Undergraduate Program	Salaried/Employee/Consultant in a sector other...	3	3	3	4	3	3	3	3	3	4	85.0	0.0	0	0	5.0	10	0.0	1	1	1	DNA test	The higher the quality of the study, the more ...	The growth rate over five years will be betwee...	5 in 100 people prior to the intervention to 6...	Vase	The medication increases recovery by 50%	Less than $102	Exactly the same as today with the money in th...	True	1000	10	1.000	59 out of 1000	9.0	20	Crosicol	< INR 500,000	25000.0	41	3	2	1	25000.0	2	2	1	2	2	2	3	1	1	1	2	1	9.0	20	1	1000	10	1.000	3	1	1	0	1	1	0	0	0	1	0	0	0	0	1	0	0	1	1	0	0	0
R_401xXJ9E3ZgU9gu	96	R_401xXJ9E3ZgU9gu	Yes, I would like to participate in the study ...	Male	33	Undergraduate Program	Stock analyst	7 (willing to take risk)	8	5	4	5	5	5	6 (strongly agree)	6 (strongly agree)	6 (strongly agree)	0.0	0.0	10	0	30.0	60	0.0	2	1	1	Fingerprint,DNA test	The higher the quality of the study, the more ...	The growth rate over five years will be betwee...	100 in 10,000 people prior to the intervention...	Vase	The medication increases recovery by 100%	More than $102	Less than today with the money in this account	False	500	1	0.001	59 out of 1000	25.0	20	They are equal	INR 500,001 – INR 15,00,000	16000000.0	33	2	2	2	16000000.0	2	1	1	2	2	2	3	2	2	2	1	2	25.0	20	3	500	1	0.001	3	1	0	0	1	1	0	0	1	0	0	0	0	1	1	1	1	0	0	1	0	0
R_40VyTmJ6i96wUP4	235	R_40VyTmJ6i96wUP4	Yes, I would like to participate in the study ...	Female	34	Post-Graduate Program	Not employed	2	0 (no investment experience)	3	2	2	3	3	3	2	3	40.0	25.0	10	10	15.0	0	0.0	1	2	1	DNA test	The higher the quality of the study, the more ...	It is not possible to predict the growth rate ...	It is not possible to determine which of the a...	Vase	The medication increases recovery by 2%	Refuse to answer	Less than today with the money in this account	Do not know	698	352	1.000	59 out of 1000	30.0	20	Crosicol	< INR 500,000	500000.0	34	2	1	1	500000.0	2	2	1	2	2	2	4	1	1	1	3	4	30.0	20	1	698	352	1.000	3	1	1	0	1	1	0	1	0	0	0	0	0	0	1	0	0	1	1	0	1	0
R_40ZEg7vX3Y7mMQl	184	R_40ZEg7vX3Y7mMQl	Yes, I would like to participate in the study ...	Female	31	Undergraduate Program	Not employed	4	6	2	2	2	1 (strongly disagree)	2	3	4	1 (strongly disagree)	30.0	30.0	10	0	20.0	10	0.0	1	1	1	HIV test,Fingerprint,DNA test,Cancer screening...	The higher the quality of the study, the more ...	The growth rate will be 0.4% on average each year	70 in 100 people prior to the intervention to ...	Vase	The medication increases recovery by 2%	More than $102	Less than today with the money in this account	True	500	10	10.000	59 out of 1000	50.0	20	They are equal	< INR 500,000	500000.0	31	2	2	1	500000.0	1	1	1	1	2	2	1	1	1	1	3	3	50.0	20	3	500	10	10.000	3	0	0	0	0	1	0	0	1	1	0	0	0	0	1	1	0	1	1	0	0	0
R_40cbsHWzTyKyFxv	53	R_40cbsHWzTyKyFxv	Yes, I would like to participate in the study ...	Male	38	Undergraduate Program	Salaried/Employee/Consultant in a sector other...	5	5	4	6 (strongly agree)	5	4	6 (strongly agree)	6 (strongly agree)	6 (strongly agree)	6 (strongly agree)	45.0	35.0	0	5	15.0	0	0.0	2	1	2	Fingerprint	The higher the quality of the study, the more ...	The growth rate over five years will be exactl...	It is not possible to determine which of the a...	Vase	The medication increases recovery by 2%	More than $102	Exactly the same as today with the money in th...	False	1000	100	25.000	9 out of 10	30.0	20	They are equal	< INR 500,000	300000.0	38	3	2	1	300000.0	2	1	2	2	2	2	2	2	2	2	3	4	30.0	20	3	1000	100	25.000	2	1	0	1	1	1	0	0	0	0	0	0	0	0	1	1	1	0	0	0	1	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
R_4xj4ficLsaoMZm9	138	R_4xj4ficLsaoMZm9	Yes, I would like to participate in the study ...	Male	20	Undergraduate Program	Salaried/Employee/Consultant in a sector other...	4	1	3	2	4	5	5	4	3	6 (strongly agree)	90.0	0.0	0	0	0.0	10	0.0	2	2	1	HIV test	Irrespective of the quality of the study, futu...	The growth rate will be 0.4% on average each year	5 in 100 people prior to the intervention to 6...	Vase	The medication increases recovery by 100%	More than $102	Exactly the same as today with the money in th...	True	500	5	10.000	9 out of 10	26.0	20	Hertinol	INR 30,00,001 – INR 50,00,000	800000.0	20	2	2	4	800000.0	1	2	2	2	2	4	1	2	2	2	1	1	26.0	20	2	500	5	10.000	2	0	1	1	1	1	0	0	1	0	0	0	0	0	1	0	1	0	0	1	0	0
R_4ygaLyBv8lHSSf7	75	R_4ygaLyBv8lHSSf7	Yes, I would like to participate in the study ...	Male	34	Undergraduate Program	Entrepreneur/Business Owner in a sector other ...	4	4	6 (strongly agree)	6 (strongly agree)	6 (strongly agree)	5	6 (strongly agree)	1 (strongly disagree)	6 (strongly agree)	6 (strongly agree)	40.0	10.0	10	10	10.0	10	10.0	1	1	2	Fingerprint	The higher the quality of the study, the more ...	The growth rate will be 0.4% on average each year	70 in 100 people prior to the intervention to ...	Vase	The medication increases recovery by 2%	More than $102	Less than today with the money in this account	Refuse to answer	500	999	100.000	9 out of 59	80.0	20	Hertinol	< INR 500,000	500000.0	34	2	2	1	500000.0	2	1	2	2	2	2	1	1	1	1	3	3	80.0	20	2	500	999	100.000	1	1	0	1	1	1	0	0	1	0	0	0	0	0	1	0	0	1	1	0	0	1
R_4yiENHF5RuMjf3P	318	R_4yiENHF5RuMjf3P	Yes, I would like to participate in the study ...	Male	42	Post-Graduate Program	Salaried/Employee/Consultant in a sector other...	5	5	6 (strongly agree)	5	6 (strongly agree)	5	6 (strongly agree)	5	6 (strongly agree)	6 (strongly agree)	20.0	50.0	0	19	10.0	0	1.0	1	2	2	DNA test	The higher the quality of the study, the more ...	The growth rate over five years will be betwee...	5 in 100 people prior to the intervention to 6...	Vase	The medication increases recovery by 2%	More than $102	Less than today with the money in this account	False	500	10	1.000	9 out of 10	25.0	20	They are equal	< INR 500,000	600000.0	42	3	1	1	600000.0	2	2	1	2	2	2	3	1	1	1	3	1	25.0	20	3	500	10	1.000	2	1	1	0	1	1	0	0	1	1	0	0	0	1	1	1	0	1	1	0	0	0
R_4zPiSk23ayGvFD4	364	R_4zPiSk23ayGvFD4	Yes, I would like to participate in the study ...	Male	70	Undergraduate Program	Salaried/Employee/Consultant in a sector other...	4	6	4	4	4	4	5	5	5	4	15.0	20.0	10	0	25.0	30	0.0	2	1	1	DNA test	The higher the quality of the study, the more ...	It is not possible to predict the growth rate ...	It is not possible to determine which of the a...	Vase	The medication increases recovery by 100%	More than $102	Less than today with the money in this account	False	500	10	1.000	59 out of 1000	25.0	20	Crosicol	INR 1500,001 – INR 30,00,000	7000000.0	70	4	2	3	7000000.0	2	2	1	2	2	2	4	2	2	2	1	4	25.0	20	1	500	10	1.000	3	1	1	0	1	1	0	1	1	1	0	0	0	1	1	0	1	0	0	1	1	0
R_8k6D0jzzHCC5X3Z	369	R_8k6D0jzzHCC5X3Z	Yes, I would like to participate in the study ...	Male	25	Undergraduate Program	Salaried/Employee/Consultant in a sector other...	7 (willing to take risk)	8	6 (strongly agree)	5	5	4	6 (strongly agree)	6 (strongly agree)	6 (strongly agree)	5	5.0	5.0	5	5	30.0	20	30.0	2	1	1	DNA test	The higher the quality of the study, the more ...	It is not possible to predict the growth rate ...	5 in 100 people prior to the intervention to 6...	Vase	The medication increases recovery by 100%	More than $102	Less than today with the money in this account	False	500	500	50.000	59 out of 1000	25.0	10	Hertinol	> INR 75,00,000	8000000.0	25	2	2	5	8000000.0	2	2	1	2	2	2	4	2	2	2	1	1	25.0	10	2	500	500	50.000	3	1	1	0	1	1	0	1	1	0	0	0	0	1	0	0	1	0	0	1	0	0

	ResponseId	age	age_rec	isced	income	wealth	Certainty_5	Uncertainty_2	RiskComprehension_5	GraphLiteracy_3	Numeracy_2	Bayesianreasoning_1	Certainty_%	Uncertainty_%	RiskComprehension_%	GraphLiteracy_%	Numeracy_%	Bayesianreasoning_%	TotalScore_18	TotalScore_%
0	R_1YkMM2lMB9aEuVL	41	3	2	1	25000.0	4	0	2	1	1	0	80.0	0.0	40.0	33.333333	50.0	0.0	8	44.444444
1	R_401xXJ9E3ZgU9gu	33	2	2	2	16000000.0	3	0	2	3	1	0	60.0	0.0	40.0	100.000000	50.0	0.0	9	50.000000
2	R_40VyTmJ6i96wUP4	34	2	1	1	500000.0	4	1	3	1	0	0	80.0	50.0	60.0	33.333333	0.0	0.0	9	50.000000
3	R_40ZEg7vX3Y7mMQl	31	2	2	1	500000.0	1	0	2	2	2	0	20.0	0.0	40.0	66.666667	100.0	0.0	7	38.888889
4	R_40cbsHWzTyKyFxv	38	3	2	1	300000.0	4	0	2	2	0	0	80.0	0.0	40.0	66.666667	0.0	0.0	8	44.444444
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
265	R_4xj4ficLsaoMZm9	20	2	2	4	800000.0	4	0	2	1	1	0	80.0	0.0	40.0	33.333333	50.0	0.0	8	44.444444
266	R_4ygaLyBv8lHSSf7	34	2	2	1	500000.0	4	0	2	1	1	1	80.0	0.0	40.0	33.333333	50.0	100.0	9	50.000000
267	R_4yiENHF5RuMjf3P	42	3	1	1	600000.0	4	0	2	3	2	0	80.0	0.0	40.0	100.000000	100.0	0.0	11	61.111111
268	R_4zPiSk23ayGvFD4	70	4	2	3	7000000.0	4	1	3	2	2	0	80.0	50.0	60.0	66.666667	100.0	0.0	12	66.666667
269	R_8k6D0jzzHCC5X3Z	25	2	2	5	8000000.0	4	1	2	1	1	0	80.0	50.0	40.0	33.333333	50.0	0.0	9	50.000000

DATA CLEANING AND WRANGLING¶

Typeform Data¶

Demographics¶

Questions¶

Scoring¶

BeSample Data¶

Scoring¶

GRAPHS¶

ISCED¶

INCOME¶

INCOME VS SCORES¶

INCOME vs SCORES w/ ISCED classification¶

INCOME vs SCORES w/ ISCED and AGE based classification¶

WEALTH¶

WEALTH vs SCORES w/ ISCED classification¶

WEALTH Quartile Number vs SCORES w/ ISCED and AGE based classification¶

	q8_2_1	q8_2_2	q8_2_3	q8_2_4	q8_2_5	q8_3	q8_4	q8_5berlin_1	q8_5london_1	q8_5paris_1	q8_6	q8_7	q9_1_1	q9_2_1	q9_3	q10_1_1	q10_2_1	q10_3_1	q10_4
1	1	1	1	2	2	4	4	2	2	2	1	2	25.0	20	4	500	10	0.10	3
4	1	1	1	1	2	2	2	2	2	2	1	2	25.0	20	3	500	10	0.10	3
7	1	1	1	1	2	2	2	2	2	2	1	3	25.0	20	3	500	10	0.10	2
8	2	1	2	2	2	2	1	2	2	2	4	2	10.0	20	1	500	100	0.10	2
9	2	1	2	2	2	2	2	2	2	2	3	1	25.0	60	1	1000	10	10.00	2
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
364	2	2	1	2	2	2	4	2	2	2	1	4	25.0	20	1	500	10	1.00	3
365	2	2	2	2	1	3	2	1	1	1	3	3	50.0	40	3	500	68	0.01	4
366	2	2	2	1	2	2	2	2	2	2	3	4	25.0	20	3	500	50	0.10	1
368	1	1	1	1	2	2	3	2	2	2	1	3	25.0	5	2	600	10	0.10	2
369	2	2	1	2	2	2	4	2	2	2	1	1	25.0	10	2	500	500	50.00	3

	ResponseId	age	age_rec	isced	income	wealth	Certainty_5	Uncertainty_2	RiskComprehension_5	GraphLiteracy_3	Numeracy_2	Bayesianreasoning_1	Certainty_%	Uncertainty_%	RiskComprehension_%	GraphLiteracy_%	Numeracy_%	Bayesianreasoning_%	TotalScore_18	TotalScore_%
0	00dn8csjzlekx6gzhrcv00dn8csj32tz	30.5	2	2	2	50000.0	0	1	2	1	0	0	0.0	50.0	40.0	33.333333	0.0	0.0	4	22.222222
1	00ujdxbfoya0donu8r00ujcjdkojc99x	30.5	2	2	1	1500000.0	1	0	2	1	1	0	20.0	0.0	40.0	33.333333	50.0	0.0	5	27.777778
2	022xoawhrghfhv4a1g022xmz7hb0m41f	50.5	3	2	1	15000000.0	2	1	2	1	1	0	40.0	50.0	40.0	33.333333	50.0	0.0	7	38.888889
3	02pxtdbyibecqqfvwlw02pxwfbane9zd	50.5	3	1	2	3000000.0	1	1	3	1	0	0	20.0	50.0	60.0	33.333333	0.0	0.0	6	33.333333
4	037aefjdt26mnjd102nz0pk037aedfhc	50.5	3	1	2	7000000.0	4	1	1	1	0	1	80.0	50.0	20.0	33.333333	0.0	100.0	8	44.444444
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
901	R_4xj4ficLsaoMZm9	20.0	2	2	4	800000.0	4	0	2	1	1	0	80.0	0.0	40.0	33.333333	50.0	0.0	8	44.444444
902	R_4ygaLyBv8lHSSf7	34.0	2	2	1	500000.0	4	0	2	1	1	1	80.0	0.0	40.0	33.333333	50.0	100.0	9	50.000000
903	R_4yiENHF5RuMjf3P	42.0	3	1	1	600000.0	4	0	2	3	2	0	80.0	0.0	40.0	100.000000	100.0	0.0	11	61.111111
904	R_4zPiSk23ayGvFD4	70.0	4	2	3	7000000.0	4	1	3	2	2	0	80.0	50.0	60.0	66.666667	100.0	0.0	12	66.666667
905	R_8k6D0jzzHCC5X3Z	25.0	2	2	5	8000000.0	4	1	2	1	1	0	80.0	50.0	40.0	33.333333	50.0	0.0	9	50.000000

	ResponseId	age	age_rec	isced	income	wealth	Certainty_5	Uncertainty_2	RiskComprehension_5	GraphLiteracy_3	Numeracy_2	Bayesianreasoning_1	Certainty_%	Uncertainty_%	RiskComprehension_%	GraphLiteracy_%	Numeracy_%	Bayesianreasoning_%	TotalScore_18	TotalScore_%
0	q3xfqbasiduvbbq3xftruekpjog3g58m	40.5	3	1	2	8000000.0	1	0	3	3	1	0	20.0	0.0	60.0	100.000000	50.0	0.0	8	44.444444
1	R_4PjnTbc2vIjogot	31.0	2	1	1	200000.0	4	0	1	0	1	0	80.0	0.0	20.0	0.000000	50.0	0.0	6	33.333333
2	R_4Ph1UNtYnaoPSX7	25.0	2	1	3	400000.0	2	0	1	0	1	1	40.0	0.0	20.0	0.000000	50.0	100.0	5	27.777778
3	n5tap3d3v5102n5tay187lv3mwzs3sjy	40.5	3	1	2	5000000.0	1	0	2	1	0	0	20.0	0.0	40.0	33.333333	0.0	0.0	4	22.222222
4	n6oeoyadt6ogysn6oeoy85zyh3urcai8	50.5	3	1	3	7500000.0	2	0	5	3	2	0	40.0	0.0	100.0	100.000000	100.0	0.0	12	66.666667
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
901	tljgvfjwoh3aj1i4hdk8wtphtljgjahd	70.5	4	3	1	2500000.0	2	1	3	3	1	0	40.0	50.0	60.0	100.000000	50.0	0.0	10	55.555556
902	y139mcxc619h0ooosy139muk14mpfpvm	30.5	2	3	1	0.0	5	0	2	0	0	1	100.0	0.0	40.0	0.000000	0.0	100.0	8	44.444444
903	R_4IY67BO1uGe00kp	39.0	3	3	2	35000.0	4	0	2	0	1	0	80.0	0.0	40.0	0.000000	50.0	0.0	7	38.888889
904	ylqbir1q5isg86n72mvz9jylqbirf512	30.5	2	3	2	50000.0	1	1	1	0	0	0	20.0	50.0	20.0	0.000000	0.0	0.0	3	16.666667
905	4h20tw0737azsz4k54h20tfe79kj50cq	21.5	2	3	1	100000.0	1	1	1	0	0	1	20.0	50.0	20.0	0.000000	0.0	100.0	4	22.222222

	Certainty_%	Uncertainty_%	RiskComprehension_%	Numeracy_%	GraphLiteracy_%	Bayesianreasoning_%
isced
1	43.488372	21.608527	43.643411	52.131783	41.731266	19.186047
2	48.707865	21.769663	44.831461	54.213483	44.850187	23.595506
3	51.764706	29.411765	42.352941	35.294118	30.392157	20.588235

	ResponseId	age	age_rec	isced	income	wealth	Certainty_5	Uncertainty_2	RiskComprehension_5	GraphLiteracy_3	Numeracy_2	Bayesianreasoning_1	Certainty_%	Uncertainty_%	RiskComprehension_%	GraphLiteracy_%	Numeracy_%	Bayesianreasoning_%	TotalScore_18	TotalScore_%
0	R_4EaOO3SIpDoBOQ7	33.0	2	2	1	2500000.0	2	2	4	3	2	1	40.0	100.0	80.0	100.000000	100.0	100.0	14	77.777778
1	tmkt2yy447ml5n5xzvtmkt2yghkchd0i	30.5	2	1	1	1500000.0	3	1	3	1	2	0	60.0	50.0	60.0	33.333333	100.0	0.0	10	55.555556
2	abucqbywl1kpsyqxf6pabucqbyuj9d1t	30.5	2	1	1	1000000.0	2	0	2	2	2	0	40.0	0.0	40.0	66.666667	100.0	0.0	8	44.444444
3	lisjggm1u63hbw5au9dlis5v74im02su	40.5	3	1	1	470000.0	1	0	2	1	1	0	20.0	0.0	40.0	33.333333	50.0	0.0	5	27.777778
4	R_4KoJRZGedem1yYC	28.0	2	1	1	400000.0	4	0	4	1	2	0	80.0	0.0	80.0	33.333333	100.0	0.0	11	61.111111
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
901	yj7bgafm7ead6tadeyj7bgafn4zliyql	30.5	2	2	5	457000.0	1	1	0	0	2	1	20.0	50.0	0.0	0.000000	100.0	100.0	5	27.777778
902	gs539g4qtok8tm7rmcjegs539g7ozj75	50.5	3	1	5	10000000.0	2	0	2	1	1	1	40.0	0.0	40.0	33.333333	50.0	100.0	7	38.888889
903	z7d97ti8bic9nnzn1h9qz7d97tmdxd06	40.5	3	1	5	70000000.0	1	1	1	2	0	0	20.0	50.0	20.0	66.666667	0.0	0.0	5	27.777778
904	x292ml1l4xbxk1apxczo1x292ml1chrx	30.5	2	1	5	10000000.0	3	0	1	2	1	0	60.0	0.0	20.0	66.666667	50.0	0.0	7	38.888889
905	R_8k6D0jzzHCC5X3Z	25.0	2	2	5	8000000.0	4	1	2	1	1	0	80.0	50.0	40.0	33.333333	50.0	0.0	9	50.000000

	Certainty_%	RiskComprehension_%	GraphLiteracy_%	Numeracy_%	Bayesianreasoning_%	TotalScore_%
income
1	53.040000	44.080000	44.000000	50.800000	20.800000	43.777778
2	43.989218	44.420485	45.193172	56.199461	20.215633	41.823899
3	43.783784	46.216216	38.963964	49.662162	21.621622	40.615616
4	39.784946	41.505376	37.634409	47.311828	20.430108	37.574671
5	40.454545	39.090909	34.090909	47.727273	27.272727	36.742424

	age	age_rec	isced	income	wealth	Certainty_5	Uncertainty_2	RiskComprehension_5	GraphLiteracy_3	Numeracy_2	Bayesianreasoning_1	Certainty_%	Uncertainty_%	RiskComprehension_%	GraphLiteracy_%	Numeracy_%	Bayesianreasoning_%	TotalScore_18	TotalScore_%
count	516.000000	516.000000	516.0	516.000000	5.160000e+02	516.000000	516.000000	516.000000	516.000000	516.000000	516.000000	516.000000	516.000000	516.000000	516.000000	516.000000	516.000000	516.000000	516.000000
mean	35.114341	2.437984	1.0	2.470930	1.070025e+07	2.174419	0.432171	2.182171	1.251938	1.042636	0.191860	43.488372	21.608527	43.643411	41.731266	52.131783	19.186047	7.275194	40.417743
std	9.188189	0.566055	0.0	1.113521	4.079317e+07	1.283429	0.595494	1.144744	0.960631	0.772164	0.394146	25.668572	29.774718	22.894880	32.021031	38.608190	39.414603	2.606535	14.480749
min	20.000000	2.000000	1.0	1.000000	0.000000e+00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	30.500000	2.000000	1.0	2.000000	4.000000e+05	1.000000	0.000000	1.000000	1.000000	0.000000	0.000000	20.000000	0.000000	20.000000	33.333333	0.000000	0.000000	5.000000	27.777778
50%	30.500000	2.000000	1.0	2.000000	1.500000e+06	2.000000	0.000000	2.000000	1.000000	1.000000	0.000000	40.000000	0.000000	40.000000	33.333333	50.000000	0.000000	7.000000	38.888889
75%	40.500000	3.000000	1.0	3.000000	7.000000e+06	3.000000	1.000000	3.000000	2.000000	2.000000	0.000000	60.000000	50.000000	60.000000	66.666667	100.000000	0.000000	9.000000	50.000000
max	70.500000	4.000000	1.0	5.000000	5.000000e+08	5.000000	2.000000	5.000000	3.000000	2.000000	1.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	15.000000	83.333333

	age	age_rec	isced	income	wealth	Certainty_5	Uncertainty_2	RiskComprehension_5	GraphLiteracy_3	Numeracy_2	Bayesianreasoning_1	Certainty_%	Uncertainty_%	RiskComprehension_%	GraphLiteracy_%	Numeracy_%	Bayesianreasoning_%	TotalScore_18	TotalScore_%
count	356.000000	356.000000	356.0	356.000000	3.560000e+02	356.000000	356.000000	356.000000	356.000000	356.000000	356.000000	356.000000	356.000000	356.000000	356.000000	356.000000	356.000000	356.00000	356.000000
mean	32.424157	2.308989	2.0	1.957865	9.234711e+06	2.435393	0.435393	2.241573	1.345506	1.084270	0.235955	48.707865	21.769663	44.831461	44.850187	54.213483	23.595506	7.77809	43.211610
std	9.745053	0.536056	0.0	1.018654	4.620976e+07	1.327408	0.613267	1.094448	0.976488	0.817884	0.425192	26.548155	30.663329	21.888957	32.549604	40.894222	42.519184	2.67459	14.858836
min	18.000000	2.000000	2.0	1.000000	0.000000e+00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.00000	5.555556
25%	25.000000	2.000000	2.0	1.000000	3.000000e+05	1.000000	0.000000	1.000000	1.000000	0.000000	0.000000	20.000000	0.000000	20.000000	33.333333	0.000000	0.000000	6.00000	33.333333
50%	30.500000	2.000000	2.0	2.000000	8.000000e+05	2.000000	0.000000	2.000000	1.000000	1.000000	0.000000	40.000000	0.000000	40.000000	33.333333	50.000000	0.000000	8.00000	44.444444
75%	38.250000	3.000000	2.0	2.000000	4.625000e+06	4.000000	1.000000	3.000000	2.000000	2.000000	0.000000	80.000000	50.000000	60.000000	66.666667	100.000000	0.000000	9.00000	50.000000
max	70.000000	4.000000	2.0	5.000000	6.000000e+08	5.000000	2.000000	5.000000	3.000000	2.000000	1.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	15.00000	83.333333

	age	age_rec	isced	income	wealth	Certainty_5	Uncertainty_2	RiskComprehension_5	GraphLiteracy_3	Numeracy_2	Bayesianreasoning_1	Certainty_%	Uncertainty_%	RiskComprehension_%	GraphLiteracy_%	Numeracy_%	Bayesianreasoning_%	TotalScore_18	TotalScore_%
count	34.000000	34.000000	34.0	34.000000	3.400000e+01	34.000000	34.000000	34.000000	34.000000	34.000000	34.000000	34.000000	34.000000	34.000000	34.000000	34.000000	34.000000	34.000000	34.000000
mean	34.176471	2.441176	3.0	1.647059	2.461516e+06	2.588235	0.588235	2.117647	0.911765	0.705882	0.205882	51.764706	29.411765	42.352941	30.392157	35.294118	20.588235	7.117647	39.542484
std	13.167270	0.746352	0.0	1.097721	5.835454e+06	1.351041	0.556920	1.121814	1.025955	0.718981	0.410426	27.020821	27.846024	22.436283	34.198516	35.949063	41.042563	2.602618	14.458987
min	16.000000	0.000000	3.0	1.000000	0.000000e+00	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	20.000000	0.000000	0.000000	0.000000	0.000000	0.000000	3.000000	16.666667
25%	22.500000	2.000000	3.0	1.000000	3.125000e+04	1.000000	0.000000	1.250000	0.000000	0.000000	0.000000	20.000000	0.000000	25.000000	0.000000	0.000000	0.000000	5.000000	27.777778
50%	30.500000	2.000000	3.0	1.000000	2.000000e+05	2.000000	1.000000	2.000000	1.000000	1.000000	0.000000	40.000000	50.000000	40.000000	33.333333	50.000000	0.000000	7.000000	38.888889
75%	40.500000	3.000000	3.0	2.000000	1.875000e+06	4.000000	1.000000	3.000000	2.000000	1.000000	0.000000	80.000000	50.000000	60.000000	66.666667	50.000000	0.000000	9.000000	50.000000
max	70.500000	4.000000	3.0	5.000000	3.000000e+07	5.000000	2.000000	4.000000	3.000000	2.000000	1.000000	100.000000	100.000000	80.000000	100.000000	100.000000	100.000000	12.000000	66.666667

	age	age_rec	isced	income	wealth	Certainty_5	Uncertainty_2	RiskComprehension_5	GraphLiteracy_3	Numeracy_2	Bayesianreasoning_1	Certainty_%	Uncertainty_%	RiskComprehension_%	GraphLiteracy_%	Numeracy_%	Bayesianreasoning_%	TotalScore_18	TotalScore_%
count	586.000000	586.0	586.000000	586.000000	5.860000e+02	586.000000	586.000000	586.000000	586.000000	586.000000	586.000000	586.000000	586.000000	586.000000	586.000000	586.000000	586.000000	586.000000	586.000000
mean	28.216724	2.0	1.503413	2.143345	7.457495e+06	2.291809	0.435154	2.163823	1.283276	1.044369	0.215017	45.836177	21.757679	43.276451	42.775882	52.218430	21.501706	7.433447	41.296928
std	3.873640	0.0	0.558529	1.062137	2.899640e+07	1.332757	0.613294	1.120343	0.966955	0.798338	0.411185	26.655134	30.664704	22.406852	32.231832	39.916923	41.118517	2.616158	14.534209
min	18.000000	2.0	1.000000	1.000000	0.000000e+00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	25.000000	2.0	1.000000	1.000000	3.000000e+05	1.000000	0.000000	1.000000	1.000000	0.000000	0.000000	20.000000	0.000000	20.000000	33.333333	0.000000	0.000000	6.000000	33.333333
50%	30.500000	2.0	1.000000	2.000000	1.000000e+06	2.000000	0.000000	2.000000	1.000000	1.000000	0.000000	40.000000	0.000000	40.000000	33.333333	50.000000	0.000000	7.000000	38.888889
75%	30.500000	2.0	2.000000	3.000000	4.000000e+06	4.000000	1.000000	3.000000	2.000000	2.000000	0.000000	80.000000	50.000000	60.000000	66.666667	100.000000	0.000000	9.000000	50.000000
max	35.000000	2.0	3.000000	5.000000	5.000000e+08	5.000000	2.000000	5.000000	3.000000	2.000000	1.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	15.000000	83.333333

	ResponseId
isced
1	516
2	356
3	34

	age	age_rec	isced	income	wealth	Certainty_5	Uncertainty_2	RiskComprehension_5	GraphLiteracy_3	Numeracy_2	Bayesianreasoning_1	Certainty_%	Uncertainty_%	RiskComprehension_%	GraphLiteracy_%	Numeracy_%	Bayesianreasoning_%	TotalScore_18	TotalScore_%
count	285.000000	285.0	285.000000	285.000000	2.850000e+02	285.000000	285.000000	285.000000	285.000000	285.000000	285.000000	285.000000	285.000000	285.000000	285.000000	285.000000	285.000000	285.000000	285.000000
mean	42.577193	3.0	1.385965	2.445614	1.463604e+07	2.277193	0.445614	2.266667	1.235088	1.042105	0.196491	45.543860	22.280702	45.333333	41.169591	52.105263	19.649123	7.463158	41.461988
std	4.501683	0.0	0.573916	1.166442	6.251005e+07	1.298837	0.576558	1.109710	0.984473	0.781599	0.398043	25.976744	28.827898	22.194192	32.815766	39.079949	39.804315	2.706840	15.038000
min	36.000000	3.0	1.000000	1.000000	0.000000e+00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000	5.555556
25%	40.500000	3.0	1.000000	2.000000	3.000000e+05	1.000000	0.000000	2.000000	0.000000	0.000000	0.000000	20.000000	0.000000	40.000000	0.000000	0.000000	0.000000	5.000000	27.777778
50%	40.500000	3.0	1.000000	2.000000	2.000000e+06	2.000000	0.000000	2.000000	1.000000	1.000000	0.000000	40.000000	0.000000	40.000000	33.333333	50.000000	0.000000	7.000000	38.888889
75%	44.000000	3.0	2.000000	3.000000	7.500000e+06	3.000000	1.000000	3.000000	2.000000	2.000000	0.000000	60.000000	50.000000	60.000000	66.666667	100.000000	0.000000	9.000000	50.000000
max	54.000000	3.0	3.000000	5.000000	6.000000e+08	5.000000	2.000000	5.000000	3.000000	2.000000	1.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	15.000000	83.333333

	age	age_rec	isced	income	wealth	Certainty_5	Uncertainty_2	RiskComprehension_5	GraphLiteracy_3	Numeracy_2	Bayesianreasoning_1	Certainty_%	Uncertainty_%	RiskComprehension_%	GraphLiteracy_%	Numeracy_%	Bayesianreasoning_%	TotalScore_18	TotalScore_%
count	233.000000	233.000000	233.000000	233.000000	2.330000e+02	233.000000	233.000000	233.000000	233.000000	233.000000	233.000000	233.000000	233.000000	233.000000	233.000000	233.000000	233.000000	233.000000	233.000000
mean	24.143777	1.991416	1.695279	1.944206	8.812035e+06	2.725322	0.484979	2.223176	1.364807	1.137339	0.227468	54.506438	24.248927	44.463519	45.493562	56.866953	22.746781	8.163090	45.350501
std	3.100287	0.131024	0.569955	1.013433	4.168456e+07	1.310469	0.637200	1.083566	0.964882	0.792217	0.420100	26.209389	31.859982	21.671324	32.162737	39.610870	42.009957	2.609501	14.497226
min	16.000000	0.000000	1.000000	1.000000	0.000000e+00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	2.000000	11.111111
25%	21.500000	2.000000	1.000000	1.000000	2.000000e+05	2.000000	0.000000	1.000000	1.000000	0.000000	0.000000	40.000000	0.000000	20.000000	33.333333	0.000000	0.000000	6.000000	33.333333
50%	23.000000	2.000000	2.000000	2.000000	5.000000e+05	3.000000	0.000000	2.000000	1.000000	1.000000	0.000000	60.000000	0.000000	40.000000	33.333333	50.000000	0.000000	8.000000	44.444444
75%	27.000000	2.000000	2.000000	2.000000	2.500000e+06	4.000000	1.000000	3.000000	2.000000	2.000000	0.000000	80.000000	50.000000	60.000000	66.666667	100.000000	0.000000	10.000000	55.555556
max	30.000000	2.000000	3.000000	5.000000	5.000000e+08	4.000000	2.000000	5.000000	3.000000	2.000000	1.000000	80.000000	100.000000	100.000000	100.000000	100.000000	100.000000	15.000000	83.333333

	age	age_rec	isced	income	wealth	Certainty_5	Uncertainty_2	RiskComprehension_5	GraphLiteracy_3	Numeracy_2	Bayesianreasoning_1	Certainty_%	Uncertainty_%	RiskComprehension_%	GraphLiteracy_%	Numeracy_%	Bayesianreasoning_%	TotalScore_18	TotalScore_%
count	673.000000	673.000000	673.000000	673.000000	6.730000e+02	673.000000	673.000000	673.000000	673.000000	673.000000	673.000000	673.000000	673.000000	673.000000	673.000000	673.000000	673.000000	673.000000	673.000000
mean	37.442051	2.524517	1.389302	2.340267	1.016252e+07	2.142645	0.423477	2.196137	1.245171	1.014859	0.203566	42.852897	21.173848	43.922734	41.505696	50.742942	20.356612	7.225854	40.143635
std	8.762724	0.592419	0.548270	1.124060	4.252269e+07	1.276100	0.587961	1.137896	0.973264	0.788545	0.402949	25.522004	29.398045	22.757913	32.442124	39.427233	40.294940	2.612832	14.515732
min	30.500000	2.000000	1.000000	1.000000	0.000000e+00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	30.500000	2.000000	1.000000	2.000000	3.750000e+05	1.000000	0.000000	1.000000	0.000000	0.000000	0.000000	20.000000	0.000000	20.000000	0.000000	0.000000	0.000000	5.000000	27.777778
50%	34.000000	2.000000	1.000000	2.000000	1.500000e+06	2.000000	0.000000	2.000000	1.000000	1.000000	0.000000	40.000000	0.000000	40.000000	33.333333	50.000000	0.000000	7.000000	38.888889
75%	40.500000	3.000000	2.000000	3.000000	6.000000e+06	3.000000	1.000000	3.000000	2.000000	2.000000	0.000000	60.000000	50.000000	60.000000	66.666667	100.000000	0.000000	9.000000	50.000000
max	70.500000	4.000000	3.000000	5.000000	6.000000e+08	5.000000	2.000000	5.000000	3.000000	2.000000	1.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	15.000000	83.333333

	ResponseId	age	age_rec	isced	income	wealth	Certainty_5	Uncertainty_2	RiskComprehension_5	GraphLiteracy_3	Numeracy_2	Bayesianreasoning_1	Certainty_%	Uncertainty_%	RiskComprehension_%	GraphLiteracy_%	Numeracy_%	Bayesianreasoning_%	TotalScore_18	TotalScore_%	Quartile Number
0	R_4MJAzsNYYA69Y8p	27.0	2	3	1	0.0	4	1	3	1	0	1	80.0	50.0	60.0	33.333333	0.0	100.0	10	55.555556	1
1	y139mcxc619h0ooosy139muk14mpfpvm	30.5	2	3	1	0.0	5	0	2	0	0	1	100.0	0.0	40.0	0.000000	0.0	100.0	8	44.444444	1
2	R_41bfnamM0zpH94i	30.0	2	1	2	0.0	4	0	2	1	1	1	80.0	0.0	40.0	33.333333	50.0	100.0	9	50.000000	1
3	2rkur9ckjvsyz36zmayz1zw2rkur9ckn	40.5	3	1	3	0.0	3	0	2	0	0	0	60.0	0.0	40.0	0.000000	0.0	0.0	5	27.777778	1
4	e7ty2tbwa1hcmte7ty683aw64pla7689	30.5	2	2	1	0.0	1	0	2	1	1	0	20.0	0.0	40.0	33.333333	50.0	0.0	5	27.777778	1
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
901	lmoqk1qcf44jqqudwkplmoqk18cao0fs	40.5	3	1	2	400000000.0	2	0	1	1	1	0	40.0	0.0	20.0	33.333333	50.0	0.0	5	27.777778	4
902	4udfypw7lf8mmgjmxr4d4udfym4sx1sf	40.5	3	1	3	500000000.0	0	0	3	0	0	0	0.0	0.0	60.0	0.000000	0.0	0.0	3	16.666667	4
903	35zux4sc18rplyz3dc85z35zux4sdfm4	21.5	2	2	4	500000000.0	2	0	1	0	1	0	40.0	0.0	20.0	0.000000	50.0	0.0	4	22.222222	4
904	wn9mk6m1k8o10l1twn9mk6b4z7l7yvhc	50.5	3	1	3	500000000.0	2	0	3	3	2	0	40.0	0.0	60.0	100.000000	100.0	0.0	10	55.555556	4
905	zcj7ldokhyo6217f9sriwizcj7ldodwt	40.5	3	2	3	600000000.0	3	1	2	1	2	1	60.0	50.0	40.0	33.333333	100.0	100.0	10	55.555556	4